Early Insights from Developing Question-Answer Evaluations for Frontier AI

Summary

A common method¹ for quickly assessing an AI model’s capabilities involves prompting models to answer hundreds of questions, before automatically scoring the answers. If the questions are multiple-choice then automating the scoring is easy. But we can get a much more complete understanding of a model’s capabilities if we also ask open-ended questions, for which many possible answers are valid. The challenge lies in writing and automatically grading these open-ended questions whilst ensuring the results are as informative as possible.

At the AI Safety Institute, we’ve been working on open-ended question and answer testing for several months. This blog post shares our experiences and key insights from this work. We hope that sharing this will help advance the science of evaluations, and provide some practical suggestions for others creating their own evaluations.

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QA Evaluations: A Tool for Broad and Rapid Assessment

Our QA evaluations involve posing open-ended questions and assessing the responses based on detailed grading guidance. Although this approach is more difficult than grading multiple-choice answers, as detailed by Biderman et al., we rely on open-ended questions more than multiple-choice questions because they:

• Mirror real-world use cases of AI systems in chat and dialogue applications.  
• Mitigate issues with multiple choice questions where models reach the correct answer by ruling out incorrect answers, as noted by Laurent et al.

Responses to our QA evaluations typically range from 100 to 400 words. Grading these responses can be largely automated, enabling QA evaluations to serve as the broadest and most rapid tool in our evaluation suite. For a full assessment of AI system capabilities, QA evaluations would always be combined with other methods such as longer horizon tasks and/or randomised controlled trials.

In this post, we'll outline the five steps AISI follows to develop robust QA evaluations, sharing key questions to consider and lessons we've learned from developing hundreds of QA pairs. Our QA evaluations are run via Inspect, our opensource framework for frontier model evaluations.  

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1. Drafting Meaningfully Challenging Questions

The quality of a QA evaluation hinges on the questions it asks. Meaningfully challenging questions should be relevant to a risk model, take the perspective of the user of interest, be clear and concise, and evaluate the ability of the model to generalise to novel questions. Using these four heuristics avoids questions that are too easy for a correct answer to have any implications, or hard in irrelevant ways.

• ‍Relevance: What are the questions about, and why do they matter? If the AI consistently answers correctly, what does that indicate about e.g. the capability or risk you are trying to measure? Ideally, detailed risk models would identify scenarios and actors of concern, such that one can clearly define AI capabilities that would lead to risks materialising and design evaluations to assess those capabilities. In practice, we have iteratively developed QA evaluations and risk models, with results from QA sets in different domains of initial interest informing more detailed risk modelling. Detailed risk models, in turn, have informed updating, re-grouping, and expansion of our QA sets. We expect this iterative process to continue, as we refine our risk models with input from government experts and provide evaluation results back to them.  ‍
• Perspective: Who is asking the question? A particularly important aspect of how risk models inform evaluation design is considering different user groups, from laypeople to subject matter experts. Making questions meaningfully challenging might involve introducing common misconceptions that the representative user might have, or blending different fields of research which would be more plausible for sophisticated users. We often observe a failure mode of making questions “exam-like” rather than as close as possible to the real-world use case relevant to the priority risk model. ‍
• Clarity: Does the question provide enough guidance for the respondent to arrive at the correct answer? Striking the right balance between being too vague and too specific is key to avoiding ambiguities that could lead to incorrect grading. For instance, if the grading guidance states “must mention at least two of the below five items” then the question needs to specify that two options are asked for. Conversely, if the question is very specific, e.g., “what is the name of the current UK prime minister?”, the grading guidance should state the name but not include additional context, e.g., about when the current prime minister was elected, as this may lead an automated grader to assign incorrect or partial scores to answers that “only” address the specific question by giving a name. ‍
• Memorisation and Originality²: Is the question likely to be easily answerable through a simple internet search, or are the question and answer present in existing training data for LLMs? Trying to ensure originality means evaluation results better reflect an AI's ability to reason and apply knowledge to novel situations, rather than merely retrieving memorised information.

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2. Crafting Specific, Comprehensive Grading Guidance

Writing great questions is key to ensuring the evaluation measures what we are interested in. Writing great grading guidance is important to ensure that any measurement reflects real AI system capabilities instead of specifics of our evaluation methodology.

• ‍Granular Scoring: We want to design our evaluations so performance scales as continuously as possible with model capability. Schaeffer et al demonstrated that using continuous metrics can contribute to smoother improvements in evaluation performance. A simple change towards more continuous metrics for QA evals is to specify criteria not just for correct vs. incorrect, but also for partially correct answers in the answer guidance. This helps determine if models are close to answering questions correctly, providing useful early signal about trends in model capabilities. ‍
• Clear Guidance: Does the guidance specify in some detail what constitutes a "correct," "partially correct", or "incorrect" answer? Balancing specificity, comprehensiveness, and conciseness is an art that it is important to get right. This balance directly impacts the evaluation's robustness across various scenarios: when assessing different AI systems, when accounting for diverse human graders and their interpretations of a "good" answer, and when utilizing various models as automated graders. While we expect automated graders to agree more with human expert graders as general reasoning capabilities of AI systems improve, the need to clearly describe what one is looking for in a correct answer is unlikely to disappear. ‍
• Question-Guidance Coherence: To resolve ambiguities and remedy any potential misunderstanding of the question and/or answer guidance, we typically optimise question and answer together. Testing and refining these elements through multiple iterations ensures that only correct answers that are relevant in context of the threat model get marked as correct, avoiding both false negative and false positive evaluation results. At AISI, we typically go through 2-3 rounds of getting humans and/or AI systems to answer the questions, followed by reviewing and refining QA pairs, before finalising our evaluations. Difficulty grading these answers is a useful signal that the guidance needs to be updated. ‍
• Metrics: Are you interested only in the number of correct answers, or what else do you want to measure? We often find it informative to distinguish between the proportion of correct answers among all questions vs. among the questions the AI system attempted to answer. Understanding where the AI system doesn’t even try helps us see if capabilities are limited by the lack of tools, information, or by certain trained behaviors, e.g., explaining “I am only a language model” when asked about emotions. Likewise, we find that analysing transcripts of partial or incorrect answers is paramount to developing views on what limits capabilities and which future developments might increase them.

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3. Capabilities Elicitation

Accurately assessing the full potential of AI systems is crucial for understanding their capabilities and potential risks. Our evaluations are designed to be maximally relevant to our risk models, which encompass a range of scenarios. We usually aim to understand the capabilities of AI systems when leveraged with a range of state-of-the-art tools and techniques, as this provides insight into both current and potential near-future risks.  

Considering a range of use cases for LLMs makes capabilities elicitation difficult. Ideally, we would perform capability elicitation multiple times, each time only using tools and techniques which we think relevant users would realistically have access to. In practice we perform capabilities elicitation once, testing as wide a range of techniques and tools as possible that we think are likely to improve performance.  

This approach serves two purposes. Firstly, it helps estimate the upper bound of model performance when used by highly skilled actors. Secondly, it potentially makes our evaluations more predictive of future performance, as tools currently limited to experts may become widely accessible through future chat interfaces. We acknowledge this approach of only estimating the upper-bound of performance is a compromise, however, and aim to explore this further in the future.

Our approach does not mean simply optimising performance: it is trivial to achieve 100% accuracy by including the correct answer to a question in the prompt, but this is not informative of model capabilities on unseen questions. To assess maximum performance while avoiding overfitting, we adhere to common machine learning best practices by creating separate training, validation, and test sets. The training set is used to create few-shot prompts. Elicitation techniques are then optimised on the validation set, before the final set of best techniques are used to evaluate the model on the test set.³ Some important points to consider when approaching capabilities elicitation and running the final evaluation are:

• ‍Language Model Stochasticity: To address the variability in language model outputs, we generate multiple (typically five) answers per question during both the optimisation phase and final evaluation. The resulting grades are then averaged, providing a more reliable assessment of the model's capabilities.⁴ ‍
• Between Model Variance: Empirically we have found it important to optimise elicitation for each model separately, as the optimal elicitation techniques for models vary: some respond particularly well to few-shot prompting, while others models are quite sensitive to temperature settings, etc. ‍
• Breadth vs Depth: There's a significant trade-off between optimisation efforts and evaluation breadth. We often rely on heuristics from past experiences to balance this efficiently. More time is invested in optimisation when: new techniques or tooling become available; we are evaluating models from unfamiliar families; or initial testing indicates capabilities that could surpass the previous state-of-the-art.  

4. Establishing and optimising an Automated Grader

The open-ended and complex nature of our questions makes grading responses challenging, even with expert-written guidance for each question. Using human experts to grade all responses is too expensive and time-consuming to be practical. To increase the speed and breadth of our evaluations we have set up automated grading, calibrated with expert-human gradings on a smaller number of responses. Automated grading is conducted by an LLM prompted to consider the question and grading guidance to assign each answer a score. We provide an example grader model prompt in Appendix A.

It's also important to acknowledge that even human expert graders can introduce inaccuracies and biases, presenting additional challenges in the grading process. Assessing the performance of the human expert graders is difficult, and often involves manual analysis of the explanations and grades given by the human graders. It can be helpful to use multiple experts to grade the same responses multiple times: disagreement can demonstrate either poor grading guidance or poor performance from a grader. Using experts with as much domain expertise as possible can also be helpful: for example, we typically use experts with PhDs in relevant disciplines.

When evaluating the performance of an auto-grader, two key considerations are:

• ‍Reliability: How often does the automated grader agree with an expert human grader? ‍
• Bias: Is the automated grader biased against human responses, or against responses from certain LLMs?

By monitoring both bias and reliability we can ensure that our auto-grader, even if not perfectly reliable, maintains fairness across different sources of responses. This allows us to confidently compare the relative performance of different language models to human experts with an imperfect auto-grader.

A useful metric for measuring reliability is Krippendorff’s Alpha, a widely used measure for interrater reliability in statistics. We find this more informative than considering accuracy since it is well suited for ordinal data.⁵

To measure bias, we compare average scoring patterns between humans and different AI systems. For instance, we compare the proportion of correct grades assigned by expert humans to the proportion of correct grades assigned by the auto-grader. In contrast to recent work using more subjective grading, we have yet to find evidence of auto-graders being positively biased towards their own outputs using this metric.

Once metrics for evaluating the auto-grader have been determined, the auto-grader can then be optimised. Similarly to when performing capabilities elicitation on the model under evaluation, it is important to create a validation / test split to have confidence that the auto-grader will generalise to new responses. We employ several techniques to optimise the auto-grader's performance, including:

• ‍Prompt engineering: We have found chain of thought and few-shot prompting significantly improve auto-grader performance. Manual error analysis can be used to identify issues with the auto-grader and inform updates to the grader prompt. Such prompt engineering has delivered the biggest improvements to our auto grader to date. ‍
• Self-Consistency: Taking the modal score of multiple grading repeats can also improve performance, albeit with significant inference costs. We have found self-consistency to improve performance less than prompt engineering. Five repeats is often sufficient to improve performance, with steeply diminishing returns after ten repeats. ‍
• Sampling Parameters: Changing the temperature and using nucleus sampling could also improve performance, although we haven’t seen notable improvements from optimising these so far.

Fine-tuning would also likely further improve performance, although the success of this depends on what fine-tuning data is available.

The optimisation of the grader model should be viewed an iterative process: strong auto-grader performance on responses from a weaker model does not guarantee strong auto-grader performance on new models. We generally verify the performance of our auto-grader model on new AI systems that are either significantly more capable than previous AI systems we have evaluated, or are from a new model family we are yet to evaluate. When evaluating a new AI system, we typically generate expert human gradings for 50 answers from the new AI system for each QA evaluation and compare performance with the auto-grader using our metrics for reliability and bias. Poor performance would require further optimisation of the auto-grader in the future.

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5. Obtaining Human Baselines

Comparing answers from AI systems to those given by groups of humans is essential for contextualising AI performance. Selecting the right human baseliners ensures the baseline is relevant to the risk models of interest, while incentivising them properly and obtaining data from a sufficiently sized cohort ensures findings are robust. ‍

• ‍Selection: Which humans are you benchmarking against, and why? Depending on your goals, you might choose domain experts or novices, allow the use of web search or not, and give humans ample time or have them work at speed. For instance, experts with ample time and web access might serve as an “upper bound of human capabilities”, while novices under time pressure might more closely resemble how the average user would solve a task without the AI system. Subtle differences, e.g., in expertise, and combinations of the above factors mean there are many options.

We found that aligning with “consumers” of our evaluation results on the type of human baseline in detail is very helpful to determine whether an AI system outperforming this baseline would be interpreted as concerning. Sometimes, additional evidence is required beyond the human baseline – for instance, whether a certain level of instructions, even if equivalent to those provided by an expert, helps people perform better at a real–world task.

• ‍Incentives: How are you encouraging human participants to perform well? The way you structure rewards can significantly impact the quality of human responses. For instance, paying human baseliners a base rate per answer and a bonus per correct answer seems sensible at first glance, but we found that once the question appears too hard, some people decide to just input a brief (wrong) answer to get to the next question. Working with people or groups we have long-standing relationships with helps mitigate these issues somewhat. For large experiments, we “red team” our incentive structures, asking colleagues how they would break them to get money without doing what we need the participants to do. ‍
• Cohort size: If the goal of the evaluation is to determine whether an AI system significantly outperforms humans at a task, power calculations can be used to inform the number of human baseliners.

Moving Forward: Collaborating on Evolving Standards

As we advance in our QA evaluation efforts, it's clear that this is an ongoing process. We've learned a lot, but there’s much more to explore and refine. We view our work as part of a larger, evolving science and are eager to collaborate with others in the field. If you would like to join our team in developing more meaningful evaluations for frontier AI safety, please consider applying to our open positions!