Alignment Between the Decision-Making Logic of LLMs and Human Cognition: A Case Study on Legal LLMs
Keywords: Explainable AI, Large Language Model, Interactions
TL;DR: This paper presents a method to evaluate the alignment between the decision-making logic of Large Language Models (LLMs) and human cognition in a case study on legal LLMs.
Abstract: This paper presents a method to evaluate the alignment between the decision-making logic of Large Language Models (LLMs) and human cognition in a case study on legal LLMs. Unlike traditional evaluations on language generation results, we propose to evaluate the correctness of the detailed decision-making logic of an LLM behind its seemingly correct outputs, which represents the core challenge for an LLM to earn human trust. To this end, we quantify the interactions encoded by the LLM as primitive decision-making logic, because recent theoretical achievements (Li & Zhang, 2023; Ren et al., 2024) have proven several mathematical guarantees of the faithfulness of the interaction-based explanation. We design a set of metrics to evaluate the detailed decision-making logic of LLMs. Experiments show that even when the language generation results appear correct, a significant portion of the internal inference logic contains notable issues.
Primary Area: alignment, fairness, safety, privacy, and societal considerations
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Submission Number: 2406
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