LLM评判者对不确定性表达具有鲁棒性吗？探究认知标记对基于LLM的评估的影响
Are LLM-Judges Robust to Expressions of Uncertainty? Investigating the effect of Epistemic Markers on LLM-based Evaluation
AI总结：
本文提出EMBER基准以评估LLM评判者对认知标记的鲁棒性，发现包括GPT-4o在内的模型在存在认知标记时均表现出鲁棒性不足及负面偏见，尤其对表达不确定性的标记偏见更强，表明LLM评判者不仅关注内容正确性，还受标记影响。
摘要：
本着诚实的原则，训练大型语言模型（LLMs）生成包含认知标记的输出的努力日益增加。然而，在存在认知标记情况下的评估在很大程度上被忽视了，这引发了一个关键问题：在LLM生成的输出中使用认知标记是否会导致意想不到的负面后果？为了解决这个问题，我们提出了EMBER，这是一个旨在评估LLM评判者在单轮和成对评估设置下对认知标记鲁棒性的基准。基于使用EMBER进行的评估，我们的发现表明，所有测试的LLM评判者（包括GPT-4o）在存在认知标记时都表现出明显的鲁棒性不足。具体而言，我们观察到对认知标记存在负面偏见，并且对表达不确定性的标记有更强的偏见。这表明LLM评判者会受到这些标记存在的影响，而不仅仅关注内容的正确性。
英文摘要：
In line with the principle of honesty, there has been a growing effort to train large language models (LLMs) to generate outputs containing epistemic markers. However, evaluation in the presence of epistemic markers has been largely overlooked, raising a critical question: Could the use of epistemic markers in LLM-generated outputs lead to unintended negative consequences? To address this, we present EMBER, a benchmark designed to assess the robustness of LLM-judges to epistemic markers in both single and pairwise evaluation settings. Our findings, based on evaluations using EMBER, reveal that all tested LLM-judges, including GPT-4o, show a notable lack of robustness in the presence of epistemic markers. Specifically, we observe a negative bias toward epistemic markers, with a stronger bias against markers expressing uncertainty. This suggests that LLM-judges are influenced by the presence of these markers and do not focus solely on the correctness of the content.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
