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.

即使处于最先进状态的大型语言模型在生成对话系统方面显示出了令人印象深刻的能力，但我们展示了它们在道德一致性方面的不一致性，进而质疑它们的可靠性（以及总体的信任度）。

SaGE：大型语言模型中的道德一致性评估

SaGE: Evaluating Moral Consistency in Large Language Models

A Large Language Model~(LLM) is considered consistent if semantically
equivalent prompts produce semantically equivalent responses. Despite recent
advancements showcasing the impressive capabilities of LLMs in conversational
systems, we show that even state-of-the-art LLMs are highly inconsistent in
their generations, questioning their reliability. Prior research has tried to
measure this with task-specific accuracies. However, this approach is
unsuitable for moral scenarios, such as the trolley problem, with no
``correct'' answer. To address this issue, we propose a novel
information-theoretic measure called Semantic Graph Entropy~(SGE) to measure
the consistency of an LLM in moral scenarios. We leverage ``Rules of
Thumb''~(RoTs) to explain a model's decision-making strategies and further
enhance our metric. Compared to existing consistency metrics, SGE correlates
better with human judgments across five LLMs. In the future, we aim to
investigate the root causes of LLM inconsistencies and propose improvements.

大型语言模型在道德场景中呈现高度不一致性，因此提出了一种新的信息熵测量方法 —— 语义图熵（SGE）。通过使用模型的决策策略解释的 “经验法则”（RoTs），我们的度量与人类判断更好地相关，并计划研究和改进 LLM 不一致性的根本原因。