SaGE: Evaluating Moral Consistency in Large Language Models
CoRR(2024)
摘要
Despite recent advancements showcasing the impressive capabilities of Large
Language Models (LLMs) in conversational systems, we show that even
state-of-the-art LLMs are morally inconsistent in their generations,
questioning their reliability (and trustworthiness in general). Prior works in
LLM evaluation focus on developing ground-truth data to measure accuracy on
specific tasks. However, for moral scenarios that often lack universally
agreed-upon answers, consistency in model responses becomes crucial for their
reliability. To address this issue, we propose an information-theoretic measure
called Semantic Graph Entropy (SaGE), grounded in the concept of "Rules of
Thumb" (RoTs) to measure a model's moral consistency. RoTs are abstract
principles learned by a model and can help explain their decision-making
strategies effectively. To this extent, we construct the Moral Consistency
Corpus (MCC), containing 50K moral questions, responses to them by LLMs, and
the RoTs that these models followed. Furthermore, to illustrate the
generalizability of SaGE, we use it to investigate LLM consistency on two
popular datasets – TruthfulQA and HellaSwag. Our results reveal that
task-accuracy and consistency are independent problems, and there is a dire
need to investigate these issues further.
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