Who's Your Judge? On the Detectability of LLM-Generated Judgments
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.
68dc97c24159d1f2418f9b54
Dawei Li
claimed_verified
2025-10-02T03:08:58.135Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6474e1afb68461d5cf7c41cc/bcoiD_qPrjHUBlB259djg.png
Dawei Li
user
wjldw
68dc97c24159d1f2418f9b55
Zhen Tan
68dc97c24159d1f2418f9b56
Chengshuai Zhao
claimed_verified
2025-10-01T10:21:39.481Z
65b2fae679954e21ac426aec
https://cdn-avatars.huggingface.co/v1/production/uploads/65b2fae679954e21ac426aec/LybSb_awygRTQinm1npUq.jpeg
Chengshuai Zhao
user
chengshuaizhao
68dc97c24159d1f2418f9b57
Bohan Jiang
68dc97c24159d1f2418f9b58
Baixiang Huang
68dc97c24159d1f2418f9b59
Pingchuan Ma
68dc97c24159d1f2418f9b5a
Abdullah Alnaibari
68dc97c24159d1f2418f9b5b
Kai Shu
68dc97c24159d1f2418f9b5c
Huan Liu
Who's Your Judge? On the Detectability of LLM-Generated Judgments
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.
J-Detector, a neural detector with linguistic and LLM-enhanced features, effectively identifies LLM-generated judgments based on scores and candidate content, addressing biases and vulnerabilities in sensitive scenarios.
Large Language Model judgment detection neural detector linguistic features LLM-enhanced features judgment scores candidate content biases detectability