Bayesian Statistical Modeling with Predictors from LLMs
This work addresses the problem of evaluating LLM alignment with human behavior for researchers in cognitive science and AI, highlighting methodological dependencies in performance assessment, but it is incremental as it builds on existing Bayesian and LLM evaluation frameworks.
The study investigated whether large language models (LLMs) can serve as explanatory models of human cognition by assessing their predictions on multiple-choice decision tasks using Bayesian statistical modeling, finding that LLMs do not capture human variance at the item-level and only some methods yield adequate fits at the condition-level.
State of the art large language models (LLMs) have shown impressive performance on a variety of benchmark tasks and are increasingly used as components in larger applications, where LLM-based predictions serve as proxies for human judgements or decision. This raises questions about the human-likeness of LLM-derived information, alignment with human intuition, and whether LLMs could possibly be considered (parts of) explanatory models of (aspects of) human cognition or language use. To shed more light on these issues, we here investigate the human-likeness of LLMs' predictions for multiple-choice decision tasks from the perspective of Bayesian statistical modeling. Using human data from a forced-choice experiment on pragmatic language use, we find that LLMs do not capture the variance in the human data at the item-level. We suggest different ways of deriving full distributional predictions from LLMs for aggregate, condition-level data, and find that some, but not all ways of obtaining condition-level predictions yield adequate fits to human data. These results suggests that assessment of LLM performance depends strongly on seemingly subtle choices in methodology, and that LLMs are at best predictors of human behavior at the aggregate, condition-level, for which they are, however, not designed to, or usually used to, make predictions in the first place.