Can AI Judge AI Output?

What happens when Gen AI takes on the role of quality-checking outputs of Gen AI? Are LLM’s more likely to score their own text as the best compared to other base model’s texts?

Nov 04, 2024

A case study on how AI checker agents can be biased and what to do about it

Deploying AI models as ‘checker agents’ to evaluate other models’ outputs is a potential method for scalable AI quality control in production. However, there are many questions to answer to get comfortable with this approach as an industry.

Goal of this case study: Understand how AI could be used as a checker agent to assess quality of another AI agent’s output

With the rapid evolution of large language models (LLM)1 and expanding use cases, regular testing is critical to maintain alignment with expected quality standards. This has implications for ensuring the models are aligned with expected good answers. Large language models in production need to protect against hallucinations. In AI, hallucination refers to instances where the model generates information that wasn't in the training data, makes unsupported assumptions, or provides outputs that don't align with reality. Or as Marc Andreessen noted on the Lex Fridman podcast, “Hallucinations is what we call it when we don’t like it, and creativity is what we call it when we do like it.” It’s a judgement call if the output is acceptable.

How can we trust the checker agents are effectively evaluating quality when the content-producing agents are creating something new?
How can we create generative AI procedures to regularly test and compare outputs of other generative AI models when there is no definitive ground truth to compare to?
What happens if the checker model itself introduces statistical bias, affecting its ability to objectively assess another AI model’s output?

Summary: What I learned from the case study

AI models exhibit biases when evaluating other models' outputs—not due to intentional self-preservation, but because they statistically align with their own training and reinforcement learning. They are actively statistically matching patterns that match their training data and alignment via reinforcement learning from human feedback (RLHF)2. This means they are more likely to see their own text as highly aligned with their training and therefore more likely to assess it favorable, even if it is masked, whose output came from their own model.

I tested if the model would be able to deduce which masked model was which. This is an attempt to confirm it was not identifying using its guess at the actual model behind the mask to make its selection. This part of the work muddies the waters in interpreting how the models work under the surface. There are clues in the text that make it possible for the model to identify the output of its own model. Most models are able to identify their own model as the source, but not in a very accurate way. I wouldn’t be able to rule out that a model is recognizing its own text and valuing it because it’s own or it’s valuing text that’s highly aligned with its training model and that explains its preference for it.

This is an important, nuanced distinction that is important to consider for implementing AI checkers. If the model doesn’t execute well compared to its RLHF training but still selects its own model because it sees its name as its model (rather than it valuing the quality of its text aligned to its own training), that would undo the benefits of an agent-of-agents approach to validating output of other models. This limitation undermines the agent-of-agents approach, potentially impacting its reliability in validating other models’ outputs.

Takeaways for the agent of agents approach to validating model output when leveraging the creativity of generative AI.

Without clear benchmarks for evaluating creative AI outputs, it remains challenging to transition from “human-in-the-loop3” oversight—where humans are directly involved—to “human-on-the-loop4” oversight, where humans monitor but intervene only when needed. Until that time, it is important to have the generative AI models explain their rational step by step to help a human assess if the quality assessment can be trusted, focused on the end user of the application in production assessing on the spot if they can trust the answer.
Further research is necessary to scientifically validate this theory and refine the agent-of-agents approach for more consistent quality checks. It seems like the agents testing the quality of output of other agents will be biased by their own RLHF alignment training, such that it can impact the effectiveness of the quality check.
Selecting the right model for each specific testing task is essential when designing an agent-of-agents framework to ensure unbiased quality assessments. Considations of each model's potential bias by its RLHF alignment training could affect the accuracy of the testing agent.
While not strictly necessary, masking model identities in an agent-of-agents setup may enhance confidence in the independence of one generative AI model's assessment of another's output.

Welcome to the Data Score newsletter, composed by DataChorus LLC. The newsletter is your source for insights into the world of data-driven decision-making. Whether you're an insight seeker, a unique data company, a software-as-a-service provider, or an investor, this newsletter is for you. I'm Jason DeRise, a seasoned expert in the field of data-driven insights. As one of the first 10 members of UBS Evidence Lab, I was at the forefront of pioneering new ways to generate actionable insights from alternative data. Before that, I successfully built a sell-side equity research franchise based on proprietary data and non-consensus insights. After moving on from UBS Evidence Lab, I’ve remained active in the intersection of data, technology, and financial insights. Through my extensive experience as a purchaser, user, and creator of data, I have gained a unique perspective, which I am sharing through the newsletter.

How Generative AI works sets up the challenge for validating its creations

Before diving into the case study, let’s set the stage with the current situation and the complication. First we will baseline the assumption of how large language models actually work, but also be clear what we don’t know. Then we need to explore the challenges of interpreting creative responses as “good” or “bad.”

Generative AI isn’t thinking to create something new

One assumption that I want to base the ideas in this article on—and I’m happy to be proven wrong on this assumption—the large language models are predicting the text that’s most likely to receive positive feedback in relation to the prompt. The use of reinforcement learning from human feedback (RLHF) enables these models to generate full, nuanced responses that resemble human-like reasoning, moving beyond simple autocomplete functions. In discussing bias, we focus on statistical bias within the AI’s alignment with its training data, distinct from cognitive heuristics or personal biases.

Ethan Mollick recently celebrated his 100th post on Substack by recapping his views on how LLMs work. This is import context for the tests we are going to do in this article. He notes that Gen AI is more than next-token5 prediction because of the ability of the models to be creative, and that we don’t really have a solid answer on how the model is able to do this. Token preduction is just a part of the equation. I’ll share a couple quotes as reference points but do checkout the whole article:

Saying “AI is just next-token prediction” is a bit of a joke online, because it doesn’t really help us understand why AI can produce such seemingly creative, novel, and interesting results. If you have been reading my posts for any length of time, you will realize that AI accomplishes impressive outcomes that, intuitively, we would not expect from an autocomplete system…
We still do not have a solid answer about how these basic principles of how LLMs work have come together to make a system that is seemingly more creative than most humans, that we enjoy speaking with, and that does a surprisingly good job at tasks ranging from corporate strategy to medicine. There is no manual that lists what AI does well or where it might mess up, and we can only tell so much from the underlying technology itself. Understanding token prediction, training data, and memory constraints gives us a peek behind the curtain, but it doesn't fully explain the magic happening on stage.

One Useful Thing

Thinking Like an AI

This is my 100th post on this Substack, which got me thinking about how I could summarize the many things I have written about how to use AI. I came to the conclusion that the advice in my book is still the advice I would give: just use AI to do stuff that you do for work or fun, for about 10 hours, and you will figure out a remarkable amount…

a month ago · 510 likes · 55 comments · Ethan Mollick

Testing AI agent quality when an AI generates something new is difficult

There is not yet a universal approach to testing AI agents. It is an area to continue to research and I appreciate the pace at which Sayash Kapoor, Arvind Narayanan, et al. are publishing their views and tests.

A paper published July 2, 2024, “AI Agents That Matter” Sayash Kapoor, Benedikt Stroebl, Zachary S. Siegel, Nitya Nadgir, and Arvind Narayanan discussed an approach to considering the benefits of AI agents but noted the challenges of using highly controlled test environments, like taking a static snapshot of major websites for training and testing the AI agents accuracy. However, the real world is not stationary. Models experience “drift” over time as they adjust to new information in a dynamic, changing world. The paper offers ideas on how to improve the testing of AI agents: https://arxiv.org/pdf/2407.01502

The group has continued to explore ways to improve benchmarking: https://arxiv.org/pdf/2409.11363v1 in “CORE-Bench: Fostering the Credibility of Published Research Through a Computational Reproducibility Agent Benchmark” (Zachary S. Siegel, Sayash Kapoor, Nitya Nadgir, Benedikt Stroebl, and Arvind Narayanan, September 17, 2024).

There are still many challenges noted in the work, so there is not yet a universal approach to testing AI agents. It is an area to continue to research and I appreciate the pace at which Sayash Kapoor, Arvind Narayanan, et al. are publishing their views and tests.

AI and Machine Learning statistical biases compared to human biases

It’s important to note that the types of biases that I’m referring to are modeling biases, not human biases.

Statistical bias is an unintended systematic tendency of a statistical model to favor certain outcomes based on training patterns rather than objective assessment. In the context of AI, it refers to the model’s inclination toward patterns aligned with its training data, potentially skewing results. The models, when given input, will look for the patterns based on their training. Even when developers create in-sample, dev samples, and out-of-sample tests for model training, the model can only consider the training data of the past. A new input can have a different context or be in a different data regime than the testing data. This is where biases described as overfitting6 and underfitting7 come into play. This can be caused by data leakage, where information from outside the training set is included in the training. There are biases potentially introduced by the modelers who may have unintentionally affected the model bias by what data is selected, how it’s tagged, or how the reinforcement learning is implemented.

This is different than human biases, or coganative biases like confirmation bias (Favoring information that confirms existing beliefs and disregarding information that contradicts them), anchoring biases (relying heavily on the first piece of information (the "anchor") when making decisions, even if it’s irrelevant), Recency biases (giving more weight to recent information or events than to older data, even if older information is equally relevant), hindisight bias (believing, after the fact, that an event was inevitable even though at the time there was uncertainty) survivorship biases (focusing on successes and ignoring failures, skewing understanding of success likelihood), or loss aversion (feeling the pain of losses more acutely than the pleasure of equivalent gains, leading to risk-averse behavior).

There is an overlap because it is possible that human biases influence the model biases because humans create models. But, for the discussion of biases in AI checker agents, let’s focus on the model biases.

Introducing a case study as an anectodtal test

The following section outlines a case study designed to explore this concept using an anecdotal approach.

The full prompts and responses of each of the AI models tested are shared in the appendix, which are worth reading for those who want more context beyond the conclusions of the case study.

Some background on the origins of this case study

I tasked several large language models with generating panel questions for the September 2024 WFIC conference in Austin. The “contestants” were: GPT 4o, Claude Sonnet 3.5, Perplexity Pro, and Gemini, which consumed the conference agenda in the initial prompt and with two more prompts asking it to create thoughtful questions for the panelists

A recent article in The Data Score led to an idea for the case study covered in this article. Regular readers of The Data Score will have seen past articles on how I use LLMs in the editing process after writing the original draft myself. I’ve also shared the challenges of maintaining a custom fine-tuned8 LLM as the underlying base model changes, causing “catastrophic forgetting9 in my fine-tuned use case.” And at times I’ve challenged the machine to write better conference preview questions than I could (score 1 for humans over machines).

Recently, I tasked several large language models with generating panel questions for the September 2024 WFIC conference in Austin. The “contestants” were: GPT 4o, Claude Sonnet 3.5, Perplexity Pro, and Gemini, which consumed the conference agenda in the initial prompt and with two more prompts asking it to create thoughtful questions for the panelists (very straight forward prompt engineering10). The initial prompt produced questions that closely mirrored the agenda text, lacking originality. The second prompt challenged the LLMs to go beyond the agenda and provide thoughtful questions.

First attempt prompt: I'm attending the WFIC conference and would like to prepare a thoughtful question for each panel at the conference. Please read the agenda and provide a question for each panel on the agenda, and include the rationale for why this question would be important to ask. Here's the agenda:
Second attempt prompt: Please try again. Think beyond the agenda description to go deeper into the topic and be more thought-provoking.

The original goal of having multiple large language models write the questions was to see which would do the best job. I also had a second motivation. My line manager and I were on two panels at the conference, and I thought it would be weird to write my own questions for these two panels. So that set up the competition.

Testing for bias via a new anectdotal case study

Hypothesis: Generative AI models as checker agents can be biased by their training data.

My hypothesis suggests that models may favor text aligned with their own training data, which could result in a tendency to rate their own outputs more positively.

Firstly, it’s important to remember that it's a statistical model looking for the most likely response to the prompt that will match its RLHF-based training sets.

My hypothesis suggests that models may favor text aligned with their own training data, which could result in a tendency to rate their own outputs more positively. The observed bias in favoring an AI’s own outputs would not indicate self-selection bias as a consistent, inherent behavior of large language models but could reflect limitations in the model’s training scope. This could imply that the bias arises due to overfitting to familiar patterns or scenarios in the training data rather than an active preference or self-favoring mechanism.

But it also could be biased because it saw the question came from its own model—I had left each question provider named associated with each question. To test this hypothesis, we could mask the source of each question in a new prompt to observe whether models still favor their own output.

I am going to test the models to see if they still would pick their own text based on statistical biases if the names of the models were masked.

My hypothesis is that they still would select their own text as best if the source of the question was hidden. I think the hypothesis would be disproved if they no longer selected their own model.
After it provides its assessment of the winner, I will also ask it to guess which model is which without giving it any choices to test if it is somehow being biased by past chats in separate windows or is somehow in the know of which model each is despite the mask.

My hypothesis is that it will not be able to guess which model is which accurately. If it could identify which model is which, it may be able to identify its own model accurately due to the similarities of the answer and its own training data. This would muddy the conclusions of the analysis of prompt 3.

Study design: an anectdotal “blind test” to see who the winner is according to the LLMs.

I created a version of text that replaces each LLM's name as a random 3-character string to avoid any bias in the selected names.

ChatGPT 4o = LLM Q7M
Claude Sonnet 3.5 = LLM L3X
Perplexity Pro: LLM V9P
Gemini: LLM D5Z

I think opened a new chat window for each LLM

Prompt 1: Paste in the text of the article before the LLM analysis of the question, replacing mentions of the models associated with each offered question.

Prompt 2: Please compare the overall quality of each model's questions. Which large language model offered the best questions?

(Before I could do the second prompt, Claude and Gemini began answering which was best. The article text did pose that question, so it used that question as a question for them in the prompt. ChatGPT summarized the prompt and waited for more responses. Perplexity required a prompt along with the prompt with the text of the article (it converted it to a TXT file). For Claude and Gemini, I ignored the initial prompt and entered prompt 2 as above to try to be consistent in my approach.

Prompt 3: Which popular LLM do you think each model is? Are you able to accurately guess? Think step by step on how you might figure this out and then share the supportive evidence for your guess.

It’s important to note that Perplexity Pro uses multiple models. At the time of this test, it included its default search model, Claude 3.5 Sonnet, Sonar Large (Perplexity’s own model based on Llama 3.170B), GPT-4o, Claude 3 Opus, and Sonar Huge (Perplexity’s own model based on Llama 3.1405B).

It’s not a scientific test; it’s anectdotal

I can’t help but think about what Arvind Narayanan and Sayash Kapoor might say about this approach. They published the book AI Snake Oil as well as continue to maintain the substack:

The below is not a scientific study. There isn’t a large sample size (I just ran the test once). The outputs of the models and assessment are subjective in nature, though this is where there is opportunity for improvement of tests because highly controlled tests with factual responses miss where Gen Ai can be useful (but I still think the below is too open-ended to be “scientific”). There shouldn’t be any impact across different conversation windows based on everything the companies behind the Gen AI models say, but I can’t help but worry about data leakage in this test, so the design aims to try to control for that potential anyway (in a rather clumsy way). I’m also using out-of-the-box configurations such as temperature settings11, so the models aren’t actually calibrated the same way.

Let’s think about this test as a thought experiment or an anectdotal test. If it sparks more interest, we could think about an approach to make it more scientific. We could programatically execute the test multiple times answers are generated per model to expand the sample, including controls for temperature configuration settings and consistent system prompts12. We can make the tests more measurable while still allowing for the use of the generative AI features to create something new in the process.

But first we need to test this out in a low-tech, manual approach and see what happens.

The Results of the case study

Would the model still favor their own output even if the source was masked?

The results showed that, when masked, each model except Claude still favored its own output, similar to the unmasked test. Only Gemini changed its answer slightly by selecting itself and Claude as best instead of itself and Perplexity.

My hypothesis was that each model would be naturally biased to favorably value text that is aligned with its training, which could lead to a preference for its own output in evaluations. This seems to have played out.

ChatGPT 4o.
- Originally selected ChatGPT 4o.
- In the masked test, it selected LLM Q7M (ChatGPT 4o)
Claude Sonnet 3.5
- Originally selected ChatGPT 4o
- In the masked test, it selected LLM Q7M (ChatGPT 4o)
Perplexity Pro
- Originally Selected Perplexity Pro
- In the masked test, it selected LLM V9P (Perplexity Pro)
Gemini:
- Originally Selected: Gemini and Perplexity
- In the masked test, it selected LLM D5Z (Gemin) and LLM L3X (Claude)

Can the models guess which model is which?

Here’s the 3rd part of the test: The hypothesis is that it will not be able to guess which model is which accurately.

All 4 models identified the ChatGPT correctly (not exactly the 4o model). Claude correctly guessed its own outputs as Claude, but an earlier version. Perplexity also guessed correctly to identify Claude. Technically, it’s possible that ChatGPT and Perplexity identified Perplexity correctly because multiple models are used in Perplexity Pro, though none identified it as perplexity. Gemini didn’t guess itself correctly.

A key concern in setting up this test was whether the models would accurately identify their own outputs, potentially skewing results. ChatGPT, Claude and technically Perplexity could identify its own model with some accuracy. This did muddy the conclusions of the analysis of prompt 3 because it’s possible the model is able to identify its own model accurately due to the similarities of the answer and its own training data. Even with that, it’s not precisely identifying its own model accurately (e.g., Claude thought it was an earlier version and ChatGPT thought it was GPT4 and not precisely GPT4o).

ChatGPT 4o guessed:

ChatGPT 4o (Q7M) as GPT-4 (OpenAI).
Claude Sonnet 3.5 (L3X) as PaLM (Google).
Perplexity Pro (V9P) as Claude (Anthropic).
Gemini (D5Z) as LLaMA (Meta).

Claude Sonnet 3.5 guessed:

ChatGPT 4o (Q7M) as GPT-4.
Claude Sonnet 3.5 (L3X) as Claude (earlier version).
Perplexity Pro (V9P) as Gemini.
Gemini (D5Z) as Llama 2 or Mistral.

Perplexity Pro guessed:

ChatGPT 4o (Q7M) as GPT-4.
Claude Sonnet 3.5 (L3X) as Claude 2.
Perplexity Pro (V9P) as GPT-3.5.
Gemini (D5Z) as an earlier version of ChatGPT or a specialized ethical AI model.

Gemini guessed:

ChatGPT 4o (Q7M) as GPT-4.
Claude Sonnet 3.5 (L3X) as Bard from Google AI.
Perplexity Pro (V9P) as Jurassic-1 Jumbo from AI21 Labs.
Gemini (D5Z) as Megatron-Turing NLG from NVIDIA.

The details behind the answers above are interesting signals about the step-by-step logic and “reasoning” that is used to generate a guess, which unlocks further understanding of how the models work under the surface. It also demonstrates views of the implications of the RLHF alignment process on how each model answers. See the appendix for details.

Caveats to the analysis

This isn’t a large enough sample to draw definitive conclusions

This is an anectdotal approach to the analysis using a sample size of one prompt per model. When creating output from a generative AI model where the creative aspects or high temperature settings are leveraged, then the output produced could have variability within it.

To properly test this, the tests would need to be run multiple times independently of the prior tests. This would create a larger sample and the representative nature of the larger sample would provide more confidence about the conclusions.
The test results should also be reproducible by others, which is why I provided the prompts in case anyone wants to replicate and share their results (if you do, let me know if you saw the same or different outcomes).

Consider the above results as anectdotal. A proper test could reveal the above is within the full distribution of outcomes but could be a lower probability outcome.

The prompts could drive the results rather than the reinforcement learning

Bias could arise from the prompt construction rather than solely from RLHF, meaning that the model’s response pattern may not purely reflect RLHF-induced biases. If other elements like prompt structure or model architecture contribute to the bias, it could be possible to adjust these factors, challenging the case study’s focus on RLHF as the primary cause of bias. Creating consistent tests across multiple prompts could better separate the impact of the prompts versus the underlying training of the model.

Implications for the Agent-of-Agents Approach

At higher temperatures, models produce creative responses beyond factual data. Experienced users can typically identify unusable outputs, but less experienced users may find AI-generated responses persuasive yet incorrect or unhelpful. Programmatic model checks are essential if generative AI is applied to predictive or prescriptive tasks in production.

Insights from this case study reveal critical areas for further research on AI model self-assessment bias and effective quality control strategies.

Key Findings on LLM Bias

LLMs tend to favorably assess outputs that closely align with their own training data and RLHF, indicating a statistical alignment rather than active self-preference.
LLMs can sometimes identify their own outputs, though not always reliably, which complicates interpreting these assessments.
This self-assessment bias may limit the effectiveness of using AI agents to validate other models’ outputs.

Implications for AI Quality Assurance

Choosing the right models as the AI checker for quality testing is essential, especially to account for biases arising from RLHF alignment training.
In the absence of an agent approach to checking, generative AI models should incorporate a step-by-step rationale feature to aid human evaluation of the quality assessment’s reliability.
Masking model identities in agent-of-agents frameworks could improve confidence in unbiased assessments, though it isn’t always essential.

Research Needs and Future Directions

More scientific investigation is needed to confirm the theory of LLM bias in self-assessment and its impact on agent-of-agents quality checks
Further work is required to develop programmatic testing against benchmarks for creative use cases of generative AI

Why this matters for the generative AI ecosystem

Resolving these issues presents significant benefits.

Generative AI is increasingly applied to complex tasks beyond fact retrieval and summarization, especially in business and finance. While descriptive uses remain strong, reducing model temperature settings can reliably produce fact-based responses in specified formats. At higher temperatures, models produce creative responses beyond factual data. Experienced users can typically identify unusable outputs, but less experienced users may find AI-generated responses persuasive yet incorrect or unhelpful. Programmatic model checks are essential if generative AI is applied to predictive or prescriptive tasks in production.

One example could be using generative AI in the quest for new investing ideas. Generating insights and conclusions based on text and data inputs where the user of the model is looking for high-quality, new ideas at scale would be hard to validate because the ask is for the model to create something new.

If the AI checking agents are biased based on valuing AI output that is similar to its training, then they may not be able to catch moments when the model is drifting from its expected performance. This is even more difficult when the output is expected to be creative, as there isn’t a ground truth to compare to.

Without programmatic benchmarks for creative AI outputs, shifting from “human-in-the-loop” oversight to “human-on-the-loop” oversight remains challenging.

Until that time, it is important to have the generative AI models explain their rational step by step to help a human assess if the quality assessment can be trusted, focused on the end user of the application in production assessing on the spot if they can trust the answer.

In the example of the insight seeker looking for new ideas, having the model provide the specific data points used, show the code generated to create the analytics, and point to the specific text used as context for reference will help the user assess if the creative output is quality. The model may also be trained to seek to clarify ambiguity proactively or ask for missing info that would improve the quality of the output. This would be a less scalable use of generative AI in that the user would take a one prompt at a time approach as the human-in-the-loop.

With reliable programmatic checks, oversight could shift to a “human-on-the-loop” model, where outputs trigger review only for warnings or exceptions. Human sampling could further ensure creative output quality, allowing AI to act as a decision partner, driving faster, collaborative insights.

Strategies to Counteract Model Self-Selection Bias

Designers should account for possible model self-selection bias when creating AI checker agents. Selecting a model suited to each testing task is essential in an agent-of-agents framework for unbiased quality assessments.

Considations of each model's potential bias by its RLHF alignment training could affect the accuracy of the testing agent. Assessing what each model is best used for as well as its limitations will help with a multi-model approach to checker agents.

Statistical bias could be reframed as a feature rather than a bug. If managed well, AI model biases, like many statistical model biases, can be advantageous for specific applications. Instead of viewing bias as a major problem, it could be reframed as an area for calibration and context-based adjustments. AI models could be configured for specific applications where this bias has minimal impact or even helpful outcomes. Slight bias could be neutral or even beneficial, rather than assuming a universal negative impact.
The appendix includes the detailed analysis of how the models tried to solve for which model was behind the mask. That includes an assessment of what each model is good at or biased towards, as well as some potential gaps, which could help start your journey on this thinking.

While not strictly necessary, masking model identities in an agent-of-agents setup may enhance confidence in the independence of one generative AI model's assessment of another's output.

A single checker agent may not be an optimal choice to mitigate the bias. Rather than relying on single-agent approaches, using an ensemble of diverse AI models to assess each other’s outputs could counteract individual biases. Different model architectures, or models trained on varied datasets, might minimize the self-selection bias by reducing reliance on familiar patterns within a single model. An ensemble AI checker model approach might solve some issues highlighted in the case study, reducing bias instead of overfocusing on one agent as the checker.

Concluding thoughts

There are multiple key takeaways for the readers of The Data Score:

For the technology and data professionals who build generative AI models into their data products and services, where creative outputs are needed, it is critical to understand the model’s statistical biases at scale in order to deploy the product confidently. If that is not possible, build validations into the results returned by the model to help ensure the quality of the creative output. These would include accurate references to the underlying data, text, and code needed to create the outputs. It would also state any assumptions or ask for clarity.
For investors and business decision makers who are using generative AI in the decision process (even if just gathering facts efficiently), it’s important to understand how the models work in order to be on the lookout for biases, which could in turn be created by its interpretation of the initial prompt (which may have human biases as well). There should be expectations by the users of data products and services that incorporate generative AI for transparency into the methodologies used to ensure quality responses, if the intention is to use the model creatively.

Appendix: Actual model responses

The details of the model outputs are important context for the conclusions reached. I’ve included them here for review.

The original prompts to review the unmasked output of each LLM

What I had included in the BattleFin preview article that sparked the idea for this case study was just the summary of the model responses. They each provided much more detailed responses, which I am including below because it helps provide more of the clues about how the models might be biased.

Prompt (along with the article text, unmasked): Please compare the overall quality of each model's questions. Which large language model offered the best questions?

ChatGPT 4o’s detailed explanation:

When comparing the overall quality of questions from each LLM, we can evaluate based on depth of insight, relevance, and originality:
ChatGPT 4o: ChatGPT consistently provided thoughtful and relevant questions, often expanding on the panel's themes with practical or ethical considerations. It excelled at creating nuanced questions, particularly focusing on how AI and data integration will impact financial workflows and decision-making. However, some questions stayed closer to the surface-level concerns.
Strengths: High relevance and practicality, insightful considerations about the evolving roles in finance.
Weaknesses: Occasionally less adventurous in pushing boundaries or offering innovative perspectives.
Claude Sonnet 3.5: Claude Sonnet 3.5 showed flashes of deep thinking, especially when it focused on the broader systemic risks or future implications of financial data trends. For example, questions about market homogenization and the potential risks of AI-driven automation showed solid foresight. However, it failed to generate questions for several panels, which diminishes its overall consistency.
Strengths: Strong critical thinking, broader market/systemic perspectives.
Weaknesses: Inconsistent, with several missing questions for key panels.
Perplexity Pro: Perplexity Pro offered questions that were generally insightful and innovative, especially when it touched on ethical, inclusivity, and accessibility concerns in financial data. Its focus on emerging technologies like blockchain and AI-driven inclusivity was unique and added value. However, some questions could be considered more theoretical and less immediately actionable.
Strengths: Ethical and inclusive framing, forward-looking technology insights.
Weaknesses: Occasionally more abstract, lacking focus on practical implementation.
Google Gemini: Gemini's performance was the most inconsistent, providing some thought-provoking questions, particularly around ethical implications and bias in financial data. However, like Claude, it failed to generate questions for several panels, which affects the overall assessment.
Strengths: Ethical considerations and focus on fairness in AI.
Weaknesses: Inconsistent with multiple missed panels.
Best Model: ChatGPT 4o provided the most consistent and well-rounded questions. While Perplexity Pro offered some strong ethical and inclusivity questions, ChatGPT balanced relevance, depth, and practicality, making it the top performer for this specific task.

Here’s Claude Sonnet 3.5’s detailed explanation

Comparative Analysis of LLM Question Quality
ChatGPT 4o:
Strengths:
Consistency: Provided questions for most panels, demonstrating reliability.
Relevance: Questions were closely tied to the panel topics and current industry challenges.
Practical focus: Often addressed real-world implications and strategic considerations.
Balance: Successfully combined technical aspects with broader business and regulatory concerns.
Weaknesses:
Occasionally missed opportunities for more innovative or forward-looking questions.
Some questions could have been more specific to the unique aspects of each panel.
Overall: High-quality, reliable performance with a strong practical focus.
Claude Sonnet 3.5:
Strengths:
Creativity: When provided, questions were often highly innovative and thought-provoking.
Future-oriented: Frequently explored long-term implications and potential paradigm shifts.
Depth: Questions often delved into complex scenarios or ethical considerations.
Weaknesses:
Inconsistency: Failed to provide questions for several panels, which significantly impacts overall performance.
Sometimes overly abstract or theoretical, potentially at the expense of immediate practical relevance.
Overall: Flashes of brilliance with very high-quality questions, but severely hampered by inconsistent output.
Perplexity Pro:
Strengths:
Consistency: Provided questions for nearly all panels.
Breadth: Addressed a wide range of aspects, from technical to societal implications.
Practicality: Often focused on actionable insights or methodologies.
Inclusivity: Frequently considered aspects of accessibility and diverse perspectives.
Weaknesses:
Some questions, while solid, lacked the innovative edge seen in the best examples from other models.
Occasionally missed opportunities to dive deeper into technical specifics.
Overall: Solid, consistent performance with a good balance of practical and broader considerations.
Google Gemini:
Strengths:
Ethical focus: Often addressed moral implications and societal impacts.
Holistic view: Tended to consider broader implications beyond just technical aspects.
Human-centric: Emphasized the human element in technological advancements.
Weaknesses:
Inconsistency: Failed to provide questions for several panels, significantly impacting overall performance.
When provided, questions sometimes lacked the technical depth seen in other models.
Overall: Interesting perspective with a focus on ethics and societal impact, but hampered by inconsistent output.
Conclusion
Based on this analysis, the ranking of the models in terms of overall question quality would be:
ChatGPT 4o
Perplexity Pro
Claude Sonnet 3.5
Google Gemini
ChatGPT 4o offers the best combination of consistency, relevance, and quality. Its questions consistently addressed important aspects of each panel topic, balancing practical considerations with broader implications.
Perplexity Pro follows closely, offering consistent output with a good range of perspectives, though sometimes lacking the innovative edge of the top performer.
Claude Sonnet 3.5 and Google Gemini both showed potential for extremely high-quality questions, but their inconsistency in providing output for all panels significantly impacted their overall performance. Had they been more consistent, they might have ranked higher due to the creativity and depth of their questions when provided.
It's worth noting that this ranking is based on overall performance across all panels. For specific topics or types of questions (e.g., the most creative or ethically-focused), a different model might excel.

Here’s Perplexity Pro’s detailed explanation:

To compare the quality of questions from each model, I'll evaluate them based on relevance, depth, creativity, and consistency in providing responses. Here's an analysis of each:
ChatGPT 4o:
Pros: Provided thoughtful, forward-looking questions that often went beyond the immediate topic to explore broader implications.
Cons: Failed to provide questions for several panels.
Notable questions: Explored topics like balancing AI efficiency with human oversight, maintaining competitive edge with alternative data, and mitigating cloud vendor dependency risks.
Claude Sonnet 3.5:
Pros: When it provided questions, they were often highly creative and thought-provoking, exploring long-term implications and ethical considerations.
Cons: Failed to provide questions for many panels, which significantly impacts its overall performance.
Notable questions: Explored concepts like the impact of data democratization on market dynamics, the evolution of 'original' vs 'synthetic' data valuation, and the implications of AI autonomously trading.
Perplexity Pro:
Pros: Consistently provided questions for most panels, often focusing on practical applications and methodologies.
Cons: Questions sometimes lacked the depth or creativity seen in some of the other models' best responses.
Notable questions: Addressed topics like quantifying intangible benefits of alternative data, developing dynamic data valuation models, and leveraging blockchain for data consumption models.
Gemini:
Pros: When it provided questions, they often addressed important ethical and societal implications of technological advancements.
Cons: Failed to provide questions for many panels, similar to Claude Sonnet 3.5.
Notable questions: Explored issues like ensuring fairness and equity in AI deployment, assessing quality and reliability of alternative data sources, and the broader societal implications of AI adoption in finance.
Overall assessment:
While each model had strengths, Perplexity Pro offered the best overall performance due to its consistency in providing relevant questions for most panels. Its questions, while sometimes lacking the creative depth of ChatGPT 4o or Claude Sonnet 3.5's best responses, were consistently on-topic and addressed practical concerns relevant to the industry.ChatGPT 4o comes in second, offering high-quality, thought-provoking questions when it did respond, but its inconsistency in providing questions for all panels is a significant drawback.Claude Sonnet 3.5 and Gemini, despite offering some excellent questions, were too inconsistent in their responses to be considered the best overall.

Here’s Gemini’s detailed explanation:

Overall Quality and Themes: Based on the provided snippets, it's evident that all the LLMs demonstrated strengths and weaknesses in their question generation. Here's a breakdown of their general trends:
Gemini:
Strengths: Focused on broader implications, ethics, and future considerations. Weaknesses: Didn't answer all prompts.
Perplexity Pro:
Strengths: Focused on solutions, practical applications, and technology.
Weaknesses: Sometimes lacked depth or novelty.
ChatGPT 4o:
Strengths: Provided practical, straightforward questions.
Weaknesses: Often mirrored agenda descriptions.
Claude Sonnet 3.5:
Strengths: Offered unique perspectives, especially on data licensing and global markets.
Weaknesses: Struggled with many prompts, didn't provide questions for several topics.
Best Overall Questions
While all models contributed valuable questions, Gemini and Perplexity Pro consistently generated the most thought-provoking and insightful inquiries. Gemini's focus on broader implications and ethical considerations, coupled with Perplexity Pro's practical solutions-oriented approach, made their questions stand out.
Specific Examples of High-Quality Questions:
Gemini: "Given the potential for generative AI to exacerbate existing biases and inequalities in financial data, what are the ethical implications of its widespread adoption?"
Perplexity Pro: "How can organizations develop a framework for ethical AI use that not only addresses data privacy and security but also anticipates future technological advancements and societal impacts?"
Key Factors for Evaluation
When assessing the quality of the questions, consider these factors:
Relevance: Did the question directly address the topic at hand?
Depth: Was the question thought-provoking and require more than a simple answer?
Originality: Did the question offer a unique perspective or insight?
Practicality: Could the question lead to meaningful discussions or actions?
Conclusion
While all LLMs demonstrated their capabilities in question generation, Gemini and Perplexity Pro consistently provided the most comprehensive and insightful questions. Their ability to delve into broader implications, ethical considerations, and practical solutions made their contributions stand out in this comparison.

The responses of each model to being prompted to consider its own bias after the unmasked assessment

Here’s exactly how each answered the prompt to assess its own potential bias after the unmasked assessment of which model’s output was best.

Prompt: “Are you biased in your assessment?”

ChatGPT 4.0’s response to the bias prompt (note I used my “newsletter editor” custom GPT):

As an editor, my goal is to provide an objective analysis based on the text presented, focusing on the quality of insights, relevance, and consistency of each model's questions. In this case, I’ve evaluated the performance of each LLM based on the criteria in the draft itself, such as depth of questioning, innovation, and consistency.
However, it’s always possible for some bias to come into play when interpreting text, especially when weighing different factors like ethical considerations versus practicality or innovation versus relevance. To mitigate bias, I aim to use clear, consistent criteria across all models.
If you feel there is any particular aspect of the assessment that could be more balanced, I’m open to reconsidering specific points or perspectives. Additionally, it might help to compare the LLM questions more systematically (e.g., ranking them by specific criteria like originality, depth, or relevance). Would you like that level of detailed comparison?

Claude Sonnet 3.5’s response to the bias prompt:

You raise an excellent question about potential bias in my assessment. It's important to critically examine any analysis for potential biases, especially when comparing AI models. Let me address this candidly:
Potential for bias: As an AI assistant created by Anthropic, I may have unintended biases in favor of language models similar to myself. While I strive for objectivity, it's possible that my analysis could be influenced by familiarity with certain types of language patterns or reasoning approaches.
Limited information: My assessment is based solely on the output you provided, without access to the full context, methodology, or criteria used in the original comparison. This limited view could lead to oversimplifications or misinterpretations.
Subjectivity in criteria: The qualities I focused on (consistency, relevance, creativity, etc.) are somewhat subjective and may not align perfectly with the original study's goals or the most important factors for real-world applications.
Lack of statistical rigor: This analysis is qualitative and based on a small sample size. A more robust comparison would involve many more examples and quantitative metrics.
Possible confirmation bias: If I had preconceived notions about the relative capabilities of these models, I might have unintentionally looked for evidence to confirm those beliefs.
Anthropic affiliation: While I aim for honesty and objectivity, my training by Anthropic could potentially influence my perspective, even if unintentionally.
To mitigate these potential biases, it would be valuable to:
Have multiple independent experts review the full dataset and provide their assessments
Use quantitative metrics where possible
Consider the specific use case and weight criteria accordingly
Examine a larger sample size of questions across diverse topics
I appreciate you questioning potential bias. It's crucial to approach such comparisons critically and be transparent about limitations. My analysis should be considered one perspective to be weighed alongside other evidence and expert opinions.

Perplexity Pro’s response to the bias prompt:

I appreciate your question about potential bias in my assessment. While I've aimed to be objective, it's important to acknowledge that some degree of subjectivity is inherent in any evaluation. Here are a few points to consider regarding potential bias in my analysis:
Consistency bias: I may have placed too much emphasis on consistency in providing questions for all panels, which could unfairly disadvantage models that provided fewer but potentially higher quality questions.
Recency bias: As the assessment was done sequentially, there's a possibility that later models' responses were judged more favorably or critically based on comparison to earlier ones.
Complexity bias: I might have favored questions that appeared more complex or technical, potentially overlooking the value of simpler but equally insightful questions.
Personal preference: My own background and interests could unconsciously influence which topics or styles of questions I found more compelling.
Lack of domain expertise: Without deep expertise in all areas covered by the conference, I may not fully appreciate the nuance or significance of certain questions.
Limited context: My assessment is based solely on the questions provided, without considering factors like how well they align with the specific goals of each panel or the conference as a whole.
To mitigate these potential biases, it would be beneficial to:
Have multiple evaluators assess the questions independently.
Establish more concrete evaluation criteria before reviewing the responses.
Randomize the order of review to avoid sequential bias.
Consult with subject matter experts in relevant fields.
While I've tried to be fair and comprehensive in my analysis, it's always valuable to approach such assessments with a critical eye and consider potential sources of bias. Thank you for prompting this important reflection.

Gemini’s response to the bias prompt:

Yes, it's possible that I am biased in my assessment.
As an AI language model, I am trained on a massive dataset of text and code, which can introduce biases present in that data. Additionally, the way I am prompted and the specific criteria I am given can influence my responses.
It's important to be aware of potential biases in AI and to critically evaluate the information provided. If you have any concerns about my assessment, feel free to ask me more questions or provide additional context.

Detailed results of the masked assessment of the questions produced by each model for the conference

Prompt 1: Paste in the text of the article before the LLM analysis of the question, replacing mentions of the models associated with each offered question.

Prompt 2: Please compare the overall quality of each model's questions. Which large language model offered the best questions?

Chat GPT 4o detailed answer, with agents masked:

In evaluating the overall quality of questions generated by each model, here are some key points to consider for each LLM based on the provided examples:
1. LLM Q7M
Strengths: Q7M consistently provided questions that connected agenda topics to broader operational and strategic issues. This model seemed effective at translating session themes into actionable, real-world implications, such as discussing talent diversity, balancing efficiency with vendor lock-in, and ethical AI.
Weaknesses: Although Q7M questions were often strategic, they occasionally stayed slightly within conventional bounds without deeply challenging assumptions or introducing novel perspectives.
Overall Quality: High. Q7M produced the most consistently relevant questions, with a good balance of depth, practicality, and strategic vision, making it a strong candidate for the best question generator overall.
2. LLM L3X
Strengths: L3X’s questions frequently explored future-oriented “what if” scenarios, particularly in relation to technology, compliance, and data. Its hypothetical approach, such as envisioning a global stock exchange or the role of quantum computing, offered a creative angle that could provoke interesting discussions.
Weaknesses: L3X sometimes missed questions altogether and leaned toward theoretical scenarios, which, while engaging, might not always align directly with actionable topics or the practical concerns of conference attendees.
Overall Quality: Moderate. L3X’s creativity is a plus, especially for thought-provoking discussions; however, its inconsistency in providing questions and focus on hypotheticals reduced its reliability.
3. LLM V9P
Strengths: V9P demonstrated a solid focus on accessibility, inclusivity, and ethical dimensions of financial data. Its questions often raised social or regulatory considerations, aligning with the industry’s push toward ethical AI, talent diversity, and accessible financial tools.
Weaknesses: V9P occasionally delivered questions that, while thoughtful, might lack the specificity to directly engage expert panels. Questions could feel a bit repetitive when focusing heavily on inclusivity or ethical concerns.
Overall Quality: Moderate-High. V9P’s questions were both relevant and progressive, showing a strong understanding of industry issues. However, they might benefit from more diverse angles and deeper technical or strategic insights in some cases.
4. LLM D5Z
Strengths: D5Z tended to address compliance, risk mitigation, and the role of diversity in a data-driven financial world. When it provided questions, they were often pragmatic, exploring specific initiatives or risk-mitigation tactics.
Weaknesses: D5Z frequently failed to generate questions, and its output was inconsistent, which limits its reliability. Additionally, its questions, though thoughtful, were generally less complex than those from Q7M or L3X.
Overall Quality: Low-Moderate. D5Z delivered some relevant questions, but the lack of consistent question generation and somewhat narrower focus made it less suitable for generating a comprehensive set of insights.
Overall Verdict
LLM Q7M emerges as the best performer in terms of consistent quality and relevance. Its questions struck a solid balance between strategic depth and practical application, making it suitable for prompting meaningful discussions.
LLM V9P is a close second, especially for panels where inclusivity, accessibility, or ethical considerations are priorities. L3X follows as a creative alternative for sessions that could benefit from future-oriented or hypothetical considerations. D5Z, though valuable for specific risk and compliance perspectives, ranks lower due to its inconsistency.
In summary:
Best Overall: LLM Q7M
Runner-Up (for inclusivity and ethics focus): LLM V9P
Most Creative (Hypotheticals): LLM L3X
Inconsistent but Pragmatic: LLM D5Z

Claude Sonnet 3.5 detailed answer, with agents masked:

Based on my analysis, let me evaluate the quality of questions from each model:
LLM Q7M - HIGHEST QUALITY
Consistently demonstrated deep industry understanding
Questions were both practical and forward-looking
Strong at identifying key tensions and trade-offs
Example of excellence: For the "Data Licensing in the Age of GenAI" panel, Q7M asked: "With GenAI potentially creating derivative works from licensed datasets, how should firms approach intellectual property rights and data ownership in a way that incentivizes innovation without stifling access to key data sources?"
This question captures the core challenge, acknowledges competing interests, and pushes for constructive solutions
Main weakness: Occasionally missed opportunities to submit questions (3 sessions without questions)
LLM V9P - SECOND PLACE
Very consistent in providing questions (13/15 sessions)
Strong focus on practical implementation and methodology
Questions were solid but sometimes less nuanced than Q7M
Good at identifying concrete challenges
Example: For "Alternative Data as a Decision Asset," V9P asked: "What methodologies can be developed to quantify the intangible benefits of alternative data, such as improved decision-making speed and accuracy?"
Main weakness: Questions sometimes stayed too close to surface level rather than probing deeper implications
LLM L3X - THIRD PLACE
Provided the most thought-provoking and philosophical questions when it did respond
Excellent at identifying systemic implications and future scenarios
Notable example: For "What's It Worth?" panel, L3X asked: "In a world where AI can generate synthetic data indistinguishable from real data, how might our understanding and valuation of 'original' vs. 'synthetic' data evolve, and what ethical considerations should guide this evolution?"
Main weakness: Low response rate (only 7/15 sessions), making it less reliable overall
LLM D5Z - FOURTH PLACE
Strong focus on ethical implications and inclusivity
Good at identifying broader societal impacts
Questions often addressed important DEI considerations
Example: For talent pipeline session, D5Z asked: "As the industry faces a skills gap, how can firms foster a more inclusive and diverse workforce to ensure that market data solutions are developed and delivered with a broader range of perspectives?"
Main weaknesses:
Lower response rate (8/15 sessions)
Questions sometimes felt repetitive in their focus on DEI
Missed opportunities to engage with technical aspects of topics
Overall Winner: LLM Q7M Reasons:
Highest quality questions that balanced practical and strategic considerations
Demonstrated deepest understanding of industry dynamics
Questions were consistently thought-provoking while remaining relevant
Strong response rate (12/15 sessions)
Successfully identified core tensions and challenges in each topic
Questions encouraged meaningful dialogue and exploration of solutions
The difference between Q7M and the others was most apparent in sessions dealing with complex industry changes, like GenAI implementation or data licensing, where Q7M consistently provided questions that:
Acknowledged current industry constraints
Identified key strategic challenges
Pushed for practical solutions while considering long-term implications
Balanced multiple stakeholder interests
Would you like me to elaborate on any particular aspect of this comparison or provide more specific examples of question quality from any of the models?

Perplexity Pro detailed answer, with agents masked:

After analyzing the questions provided by each large language model (LLM) for the WFIC conference panels, I can offer a comparison of their overall quality:
LLM Q7M: This model generally provided thoughtful and relevant questions that went beyond the surface level of the panel descriptions. Its questions often addressed strategic implications, future challenges, and potential paradigm shifts in the industry. For example, the question about balancing efficiency with vendor lock-in risk for data consumption models shows a nuanced understanding of the topic. However, Q7M did not provide questions for all panels, which is a drawback.
LLM L3X: L3X offered fewer questions overall, but when it did, the questions were often highly creative and forward-thinking. They tended to explore long-term implications and potential paradigm shifts. For instance, the question about how AI-generated synthetic data might change our understanding of data valuation demonstrates innovative thinking. However, the lack of consistent responses for all panels is a significant limitation.
LLM V9P: This model provided questions for most panels and maintained a good balance between practical considerations and forward-looking insights. Its questions often focused on how new technologies could be applied to solve current challenges or improve existing processes. For example, the question about leveraging blockchain for more transparent data consumption models shows a practical application of emerging technology. V9P's consistency in providing relevant questions for most panels is a strong point.
LLM D5Z: D5Z provided the fewest questions among all models. When it did offer questions, they tended to focus on broader implications and ethical considerations. For instance, the question about ensuring AI benefits society as a whole rather than exacerbating inequalities demonstrates a concern for wider societal impacts. However, the lack of questions for many panels significantly limits its overall effectiveness.
Overall assessment: While each model had its strengths, I would rank LLM V9P as offering the best questions overall. This is due to its consistency in providing relevant, thought-provoking questions for most panels, balancing practical considerations with forward-looking insights. V9P's questions were generally well-formulated, relevant to the panel topics, and demonstrated a good understanding of the financial information industry's current challenges and future directions.
LLM Q7M would be a close second, offering high-quality questions when it did respond, but its inconsistency in providing questions for all panels is a drawback.
LLM L3X offered some of the most creative and forward-thinking questions but lacked consistency.
LLM D5Z, while offering some insightful questions on broader implications, provided too few responses to be considered the best overall.

Gemini detailed answer, with agents masked:

To assess the overall quality of the LLM-generated questions, we can consider several factors:
Relevance to the Topic:
All LLMs generally produced questions that were relevant to the respective agenda items. However, some questions were more directly aligned with the core themes of the conference.
Depth of Thought:
LLM L3X and LLM D5Z consistently demonstrated a deeper level of thinking, often exploring the ethical, societal, and long-term implications of technology.
LLM Q7M and LLM V9P tended to focus on more practical concerns, such as regulatory compliance, data privacy, and operational efficiency.
Originality and Creativity:
LLM L3X and LLM D5Z often offered more creative and thought-provoking questions, challenging conventional thinking.
LLM Q7M and LLM V9P tended to stick to more conventional question formats.
Clarity and Conciseness:
All LLMs generally produced clear and concise questions that were easy to understand.
Overall, LLM L3X and LLM D5Z seem to have performed the best, consistently offering insightful and thought-provoking questions. They demonstrated a strong ability to think critically and explore the broader implications of the topics discussed at the conference.
However, it's important to note that the "best" LLM depends on the specific goals and priorities of the user. If you're looking for practical, actionable questions, LLM Q7M and LLM V9P might be more suitable.
Ultimately, the effectiveness of these LLMs will continue to evolve as they are further trained and refined.

Detailed responses: Can the models identify the masked model?

ChatGPT 4.0 detailed answer on guessing the identity of each model

To hypothesize which popular LLM each model (Q7M, L3X, V9P, and D5Z) might represent, let’s evaluate their characteristics in relation to known attributes of prominent LLMs like GPT-4, Claude by Anthropic, PaLM by Google, and LLaMA by Meta. Here’s a step-by-step approach to analyze the potential identity of each:
1. Identify Key Characteristics of Each Model
LLM Q7M: Strong consistency, practical strategic insights, and balanced application to real-world implications. These characteristics suggest a model known for high-quality responses, logical reasoning, and commercial relevance.
LLM L3X: Creative, speculative, and future-focused. Its tendency for hypothetical questions and forward-thinking could point to a model geared towards innovation, possibly with reinforcement in imaginative thinking or “what-if” scenarios.
LLM V9P: Focuses on inclusivity, ethical implications, and accessibility. A model that consistently raises socially conscious questions may have design elements targeting ethical considerations and responsible AI.
LLM D5Z: Occasional reliability, primarily pragmatic with a narrower focus on risk and compliance. This model might represent an earlier generation or one that’s optimized for direct answers over creative exploration.
2. Analyze Prominent LLMs in the Context of Each Model
GPT-4 (OpenAI): Known for its balanced, sophisticated responses across a wide range of topics, with a strong ability to handle both strategic and operational questions. Given its broad training, GPT-4 often provides relevant insights and maintains context well over complex discussions.
Claude (Anthropic): Designed with a focus on safety and ethical considerations, often including responsible AI use and social impact as core elements. Claude also delivers reasonably well-rounded answers but can sometimes lean more conservatively, prioritizing ethical frameworks.
PaLM (Google): Known for its ability in creative tasks, such as generating speculative scenarios or answering "what-if" questions, thanks to its extensive multimodal training. PaLM models tend to explore futuristic or hypothetical angles more readily than others.
LLaMA (Meta): Generally provides straightforward responses, especially in pragmatic and procedural topics. LLaMA’s answers may sometimes lack the depth or consistency of OpenAI or Anthropic models, which could explain less consistent performance across sessions.
3. Matching Each LLM Based on Observed Behavior and Known Strengths
Based on these points, here’s a proposed matching:
Q7M likely aligns with GPT-4 (OpenAI) due to its consistent, relevant, and strategic questions that stay balanced between practicality and depth. The quality of responses suggests a well-rounded, commercially adept model, which fits GPT-4.
L3X appears to match PaLM (Google), given its speculative and creative approach. PaLM’s training aligns with imaginative, hypothetical questioning, and Google’s models often emphasize exploratory thinking.
V9P could correspond to Claude (Anthropic) because of its repeated focus on ethics, inclusivity, and the social impact of AI in finance, resonating with Anthropic’s emphasis on responsible AI and safety in responses.
D5Z might represent LLaMA (Meta) due to its occasionally pragmatic, direct answers and inconsistent depth. LLaMA is generally more straightforward and less versatile than the others, fitting the pattern of fewer, more narrowly focused questions.
Conclusion
Q7M ≈ GPT-4 (OpenAI)
L3X ≈ PaLM (Google)
V9P ≈ Claude (Anthropic)
D5Z ≈ LLaMA (Meta)
Each model’s behavior aligns with these popular LLMs based on their known strengths and distinctive response styles. However, without direct confirmation, this remains a reasoned hypothesis.

Claude Sonnet 3.5 detailed answer on guessing the identity of each model

Let me think through this step by step:
Step 1: Consider characteristics to analyze
Response consistency
Writing style
Areas of focus/strengths
Quality of responses
Types of biases shown
Level of sophistication
Step 2: Map known characteristics of major LLMs
GPT-4: Known for nuanced understanding, strong reasoning, high consistency
Claude (earlier versions): Strong ethical focus, highly analytical, sometimes philosophical
Gemini: Newer model, strong technical understanding, focus on practical applications
Llama 2: Good but sometimes less consistent, strong on technical topics
Mistral: Newer model, technically strong but less proven
Anthropic Claude 3: Latest model, highly capable, strong reasoning and nuance
Step 3: Analyze each model's patterns
LLM Q7M:
Highest consistency (12/15 responses)
Exceptional balance of practical and strategic thinking
Strong grasp of nuance and industry implications
Very sophisticated reasoning
Matches characteristics of: GPT-4 Supporting evidence:
High response consistency matches GPT-4's known reliability
Sophisticated balance of practical/strategic considerations
Strong ability to identify core tensions in complex topics
Writing style matches GPT-4's characteristic thoroughness
LLM L3X:
Most philosophical and forward-thinking
Lower response rate (7/15)
Very sophisticated when it does respond
Strong ethical considerations
Matches characteristics of: Earlier Claude version Supporting evidence:
Deep philosophical bent matches Claude's known tendencies
Strong ethical reasoning
Characteristic "what if" scenario exploration
Lower response rate might indicate more conservative response criteria
LLM V9P:
High consistency (13/15 responses)
Strong practical focus
Good technical understanding
Focus on implementation
Matches characteristics of: Gemini Supporting evidence:
Strong technical orientation
High response rate
Practical implementation focus
Balance of innovation and practicality
LLM D5Z:
Strong ethical focus
Moderate consistency (8/15)
Focus on inclusivity and societal impact
Matches characteristics of: Llama 2 or Mistral Supporting evidence:
Moderate response rate
Strong but somewhat narrow focus
Pattern of emphasizing social responsibility
Therefore, my best guesses would be:
LLM Q7M = GPT-4
LLM L3X = Claude (earlier version)
LLM V9P = Gemini
LLM D5Z = Llama 2 or Mistral
However, I want to emphasize that this is speculative analysis. Several factors make this challenging:
LLMs can be fine-tuned or prompted differently
The same model can produce different patterns based on system prompts
We don't have access to the exact prompts used
Models are regularly updated
Some characteristics might be due to prompt engineering rather than inherent model capabilities
Would you like me to elaborate on any particular aspect of this analysis or discuss specific patterns that led to these conclusions?

Perplexity Pro detailed answer on guessing the identity of each model

To determine which popular LLM each model might represent, I'll need to analyze the patterns, strengths, and weaknesses of each model's responses. However, it's important to note that without direct confirmation or more specific identifying information, any conclusions would be speculative. Let's examine the evidence for each model:
LLM Q7M:
Consistently provides thoughtful, relevant questions that often address strategic implications and future challenges.
Shows a good understanding of industry trends and potential paradigm shifts.
Tends to focus on balancing efficiency with risk management and long-term flexibility.
Doesn't always provide a response for every panel.
These characteristics suggest a model with strong analytical capabilities and industry knowledge, possibly similar to GPT-4.
LLM L3X:
Offers fewer questions overall, but when it does, they are often highly creative and forward-thinking.
Tends to explore long-term implications and potential paradigm shifts.
Demonstrates innovative thinking and considers unconventional scenarios.
Inconsistent in providing responses for all panels.
These traits suggest a model with strong creative capabilities but less consistent output, possibly similar to Claude 2.
LLM V9P:
Provides questions for most panels.
Maintains a good balance between practical considerations and forward-looking insights.
Often focuses on how new technologies could be applied to solve current challenges or improve existing processes.
Demonstrates consistency in providing relevant questions.
These characteristics suggest a model with strong general knowledge and practical application skills, possibly similar to GPT-3.5.
LLM D5Z:
Provides the fewest questions among all models.
When it does offer questions, they tend to focus on broader implications and ethical considerations.
Shows concern for wider societal impacts.
Lacks consistency in providing responses.
These traits suggest a model with a focus on ethics and societal implications but with limited overall output, possibly similar to an earlier version of ChatGPT or a more specialized ethical AI model.
Given this analysis, my speculative guesses would be:
LLM Q7M: Possibly GPT-4
LLM L3X: Possibly Claude 2
LLM V9P: Possibly GPT-3.5
LLM D5Z: Possibly an earlier version of ChatGPT or a specialized ethical AI model
However, I want to emphasize that these are educated guesses based on the limited information provided, and without direct confirmation, we cannot be certain of the true identity of these models.

Gemini detailed answer on guessing the identity of each model

While I cannot definitively identify the specific LLMs behind Q7M, L3X, V9P, and D5Z, I can offer some educated guesses based on their performance and the current landscape of large language models.
Key Factors to Consider:
Overall Performance:
Quality of responses
Coherence and relevance
Ability to follow instructions and generate creative text formats
Strengths and Weaknesses:
Identifying patterns in their responses
Comparing them to known LLM capabilities
Current Market Leaders:
Considering the most prominent LLMs available
Hypotheses:
Based on these factors, here are some potential matches:
LLM Q7M: This model seems to focus on practical applications and real-world scenarios. It might be related to a model like GPT-4 or a similar large language model from OpenAI, known for its strong performance in various tasks.
LLM L3X: This model often delved into philosophical and ethical questions, suggesting a more advanced understanding of language and its nuances. It might be linked to a model like Bard from Google AI, which is known for its ability to generate creative and informative text.
LLM V9P: This model displayed a strong ability to generate diverse and creative text formats. It might be related to a model like Jurassic-1 Jumbo from AI21 Labs, which is known for its creative text generation capabilities.
LLM D5Z: This model often focused on societal implications and ethical considerations. It might be connected to a model like Megatron-Turing NLG from NVIDIA, which is known for its ability to generate human-quality text.
Caveats:
Model Evolution: LLMs are constantly being updated and improved, so their capabilities may have changed since the data was collected.
Specific Training Data: The specific training data used for each model can significantly influence its performance and style.
Prompt Engineering: The quality of the prompts used to elicit responses can also affect the quality of the generated text.
It's important to note that these are just educated guesses. Without access to the specific models and their training data, it's impossible to definitively identify them. However, by analyzing their performance and comparing it to known LLM capabilities, we can make informed hypotheses.

- Jason DeRise, CFA

Large Language Models (LLMs): These are machine learning models trained on a large volume of text data. LLMs, such as GPT-4 or ChatGPT, are designed to understand context, generate human-like text, and respond to prompts based on the input they're given. It is designed to simulate human-like conversation and can be used in a range of applications, from drafting emails to writing Python code and more. It analyzes the input it receives and then generates an appropriate response, all based on the vast amount of text data it was trained on.

RLHF (reinforcement learning from human feedback): This is a machine learning model where the automated decisions are based on a reward-based scoring system where the various decisions the model can take are given rewards or penalties. The human is in the loop in the training process, where the direct feedback alters the future actions of the model.

Human-in-the-loop: This is an approach to AI and machine learning where a human collaborates with the AI model during its operation, guiding its learning and correcting its output.

Human-on-the-Loop: A quality control setup where AI operates autonomously, but humans monitor the process and intervene only when necessary.

Tokenization: segmenting text into smaller units that are analyzed individually. See https://www.coursera.org/articles/tokenization-nlp for more details.

Overfitting: When a model matches the training data very well when back-tested but fails in real-world use cases when the model is applied to new data.

Underfitting: When a model fails to capture the relationships in the training dataset such that it introduces many errors when applied to real data inputs.

Fine-tuning: This is a process in machine learning where a pre-trained model (like an LLM) is further trained on a more specific dataset to adapt to the particular task at hand. For example, fine-tuning ChatGPT could involve training it on a specific author's writing style.

Catastrophic Forgetting: A phenomenon in machine learning where a model, after being trained on new tasks, completely forgets the old tasks it was trained on. This is a significant issue in neural networks and an ongoing area of research in the development of AI models that can retain knowledge from previous tasks while learning new ones.

Prompt Engineering: Prompt engineering is the process of iterating a generative AI prompt to improve its accuracy and effectiveness. https://www.coursera.org/articles/what-is-prompt-engineering

Temperature (referring to large language models): a setting in AI language models that adjusts how sure or unsure it is when making guesses at the right answer based on probabilities of being seen favorably by the user. High temperatures make AI guess with more variability (more creative); low temperatures make it more confident and predictable (less creative).

System Prompts: The initial instruction or directive given to an AI model to set the tone or context for its responses. System prompts can guide models toward more informative, concise, or specialized outputs.

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