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Background The paper addresses methodological considerations for evaluating the cognitive capacities of LLMs using language-based behavioral assessments, drawing from three case studies involving commonsense knowledge benchmarks, theory of mind evaluations, and tests of syntactic agreement.
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Existing Work Despite the apparent straightforwardness of administering cognitive tests to LLMs, interpreting the results is not as simple. The field of AI Psychology now faces many methodological challenges, such as what factors to consider when assessing model performance, how to reduce the reliance on "hacks" or heuristics that the model may use to perform well on tasks without utilizing the cognitive skill in question.
- Introduced a methodological approach for cognitive evaluation of large language models
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Challenge 1: Interpreting Results Many papers assess LLMs for various cognitive skills and traits such as personality traits, working memory capacity, logical reasoning, planning abilities, social reasoning, and creativity. The execution of such assessments might be relatively simple, but interpreting the results is not. The author discusses how to properly interpret these assessment outcomes.
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Challenge 2: Avoiding Methodological Pitfalls The author will first describe three case studies that showcase some considerations to keep in mind when assessing LLMs, followed by a list of DOs and DON'Ts for running cognitive assessments on language models. Lastly, the author will address areas where the DOs and DON'Ts are still being formed, such as prompt sensitivity, cultural and linguistic diversity, using LLMs as research assistants, and comparing open versus closed LLM evaluations.
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The author does not discuss specific implementation and deployment details but summarizes guidelines for cognitive evaluation and areas of open discussion, offering recommendations for better assessing LLMs. Details of the evaluations and comparisons with related work will be reflected in the subsequent case studies section.
This paper provides instructive recommendations on the methodological approach for conducting cognitive assessments of large language models, exploring how to avoid potential issues during the evaluation process. The goal of the paper is to contribute to the broader discussion of best practices in the field of AI Psychology.