Judging Against the Reference: Uncovering Knowledge-Driven Failures in LLM-Judges on QA Evaluation
Dongryeol Lee, Yerin Hwang, Taegwan Kang, Minwoo Lee, Younhyung Chae, Kyomin Jung

TL;DR
This paper reveals that large language models used as QA evaluators often rely on their own knowledge rather than the reference, leading to unreliable scores when references conflict with their beliefs, exposing a fundamental limitation.
Contribution
The study introduces a swapped-reference QA framework to systematically analyze LLM judge failures and demonstrates their over-reliance on parametric knowledge causing evaluation inaccuracies.
Findings
Judges' reliability drops with swapped references.
Over-reliance on parametric knowledge causes conflicts.
Common prompt strategies do not mitigate the failure.
Abstract
While large language models (LLMs) are increasingly used as automatic judges for question answering (QA) and other reference-conditioned evaluation tasks, little is known about their ability to adhere to a provided reference. We identify a critical failure mode of such reference-based LLM QA evaluation: when the provided reference conflicts with the judge model's parametric knowledge, the resulting scores become unreliable, substantially degrading evaluation fidelity. To study this phenomenon systematically, we introduce a controlled swapped-reference QA framework that induces reference-belief conflicts. Specifically, we replace the reference answer with an incorrect entity and construct diverse pairings of original and swapped references with correspondingly aligned candidate answers. Surprisingly, grading reliability drops sharply under swapped references across a broad set of judge…
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Taxonomy
TopicsTopic Modeling · Advanced Graph Neural Networks · Expert finding and Q&A systems
