TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them
The adoption of Large Language Models (LLMs) as automated evaluators
(LLM-as-a-judge) has revealed critical inconsistencies in current evaluation
frameworks. We identify two fundamental types of inconsistencies: (1)
Score-Comparison Inconsistency, where lower-rated responses outperform
higher-scored ones in pairwise comparisons, and (2) Pairwise Transitivity
Inconsistency, manifested through circular preference chains (A>B>C>A) and
equivalence contradictions (A=B=C\neq A). We argue that these issues come from
information loss in discrete rating systems and ambiguous tie judgments during
pairwise evaluation. We propose TrustJudge, a probabilistic framework that
addresses these limitations through two key innovations: 1)
distribution-sensitive scoring that computes continuous expectations from
discrete rating probabilities, preserving information entropy for more precise
scoring, and 2) likelihood-aware aggregation that resolves transitivity
violations using bidirectional preference probabilities or perplexity. We also
formalize the theoretical limitations of current LLM-as-a-judge frameworks and
demonstrate how TrustJudge's components overcome them. When evaluated with
Llama-3.1-70B-Instruct as judge using our dataset, TrustJudge reduces
Score-Comparison inconsistency by 8.43% (from 23.32% to 14.89%) and Pairwise
Transitivity inconsistency by 10.82% (from 15.22% to 4.40%), while maintaining
higher evaluation accuracy. Our work provides the first systematic analysis of
evaluation framework inconsistencies in LLM-as-a-judge paradigms, offering both
theoretical insights and practical solutions for reliable automated assessment.
The framework demonstrates consistent improvements across various model
architectures and scales, enabling more trustworthy LLM evaluation without
requiring additional training or human annotations. The codes can be found at
https://github.com/TrustJudge/TrustJudge.
68d5ec328ccd91bdd39ffccc
Yidong Wang
admin_assigned
2025-09-26T16:09:59.108Z
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Yidong Wang
user
qianlanwyd
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Yunze Song
admin_assigned
2025-09-26T16:10:09.684Z
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YunzeSong
user
YunzeSong
68d5ec328ccd91bdd39ffcce
Tingyuan Zhu
68d5ec328ccd91bdd39ffccf
Xuanwang Zhang
68d5ec328ccd91bdd39ffcd0
Zhuohao Yu
admin_assigned
2025-09-26T16:10:23.995Z
61e24808b31e7cc38eb84d37
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Zhuohao Yu
user
zhuohaoyu
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Hao Chen
68d5ec328ccd91bdd39ffcd2
Chiyu Song
admin_assigned
2025-09-26T16:10:30.847Z
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/avatars/8797243b26b270f0e2a5c668a07a2cfa.svg
Chiyu Song
user
ChiyuSONG
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Qiufeng Wang
admin_assigned
2025-09-26T16:10:39.339Z
640f3984c025ddf618978589
/avatars/f8b11defb4a115f1af8d68b6d1228571.svg
Qiufeng Wang
user
bruceqfw
68d5ec328ccd91bdd39ffcd4
Cunxiang Wang
admin_assigned
2025-09-26T16:10:46.135Z
65eaf755ab0a6a90da55ab58
/avatars/a46890a9d067a913513edf3759f12c85.svg
Cunxiang Wang
user
wangcunxiang
68d5ec328ccd91bdd39ffcd5
Zhen Wu
68d5ec328ccd91bdd39ffcd6
Xinyu Dai
68d5ec328ccd91bdd39ffcd7
Yue Zhang
68d5ec328ccd91bdd39ffcd8
Wei Ye
68d5ec328ccd91bdd39ffcd9
Shikun Zhang
TrustJudge: Inconsistencies of LLM-as-a-Judge and How to Alleviate Them
The adoption of Large Language Models (LLMs) as automated evaluators
(LLM-as-a-judge) has revealed critical inconsistencies in current evaluation
frameworks. We identify two fundamental types of inconsistencies: (1)
Score-Comparison Inconsistency, where lower-rated responses outperform
higher-scored ones in pairwise comparisons, and (2) Pairwise Transitivity
Inconsistency, manifested through circular preference chains (A>B>C>A) and
equivalence contradictions (A=B=C\neq A). We argue that these issues come from
information loss in discrete rating systems and ambiguous tie judgments during
pairwise evaluation. We propose TrustJudge, a probabilistic framework that
addresses these limitations through two key innovations: 1)
distribution-sensitive scoring that computes continuous expectations from
discrete rating probabilities, preserving information entropy for more precise
scoring, and 2) likelihood-aware aggregation that resolves transitivity
violations using bidirectional preference probabilities or perplexity. We also
formalize the theoretical limitations of current LLM-as-a-judge frameworks and
demonstrate how TrustJudge's components overcome them. When evaluated with
Llama-3.1-70B-Instruct as judge using our dataset, TrustJudge reduces
Score-Comparison inconsistency by 8.43% (from 23.32% to 14.89%) and Pairwise
Transitivity inconsistency by 10.82% (from 15.22% to 4.40%), while maintaining
higher evaluation accuracy. Our work provides the first systematic analysis of
evaluation framework inconsistencies in LLM-as-a-judge paradigms, offering both
theoretical insights and practical solutions for reliable automated assessment.
The framework demonstrates consistent improvements across various model
architectures and scales, enabling more trustworthy LLM evaluation without
requiring additional training or human annotations. The codes can be found at
https://github.com/TrustJudge/TrustJudge.
TrustJudge, a probabilistic framework, addresses inconsistencies in LLM-as-a-judge evaluation by using distribution-sensitive scoring and likelihood-aware aggregation, improving accuracy and reliability.
Large Language Models LLM-as-a-judge Score-Comparison Inconsistency Pairwise Transitivity Inconsistency distribution-sensitive scoring likelihood-aware aggregation TrustJudge Llama-3.1-70B-Instruct