Who's Your Judge? On the Detectability of LLM-Generated Judgments
Authors: name: Dawei Li, name: Zhen Tan, name: Chengshuai Zhao, name: Bohan Jiang, name: Baixiang Huang, name: Pingchuan Ma, name: Abdullah Alnaibari, name: Kai Shu, name: Huan Liu
Organization: Data Mining and Machine Learning lab

Large Language Model (LLM)-based judgments leverage powerful LLMs to
efficiently evaluate candidate content and provide judgment scores. However,
the inherent biases and vulnerabilities of LLM-generated judgments raise
concerns, underscoring the urgent need for distinguishing them in sensitive
scenarios like academic peer reviewing. In this work, we propose and formalize
the task of judgment detection and systematically investigate the detectability
of LLM-generated judgments. Unlike LLM-generated text detection, judgment
detection relies solely on judgment scores and candidates, reflecting
real-world scenarios where textual feedback is often unavailable in the
detection process. Our preliminary analysis shows that existing LLM-generated
text detection methods perform poorly given their incapability to capture the
interaction between judgment scores and candidate content -- an aspect crucial
for effective judgment detection. Inspired by this, we introduce
J-Detector, a lightweight and transparent neural detector augmented
with explicitly extracted linguistic and LLM-enhanced features to link LLM
judges' biases with candidates' properties for accurate detection. Experiments
across diverse datasets demonstrate the effectiveness of J-Detector
and show how its interpretability enables quantifying biases in LLM judges.
Finally, we analyze key factors affecting the detectability of LLM-generated
judgments and validate the practical utility of judgment detection in
real-world scenarios.