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evaluation alignment

Evaluation alignment is the degree of agreement and consistency between automated evaluation systems and human judgments or established quality benchmarks when assessing model outputs. In artificial intelligence and machine learning, automated evaluators—such as large language models serving as judges or algorithmic scoring metrics—are frequently deployed to score, rank, and compare candidate responses. Evaluation alignment measures how accurately these automated decisions reflect expert human consensus and normative criteria, focusing on identifying and mitigating systematic evaluation errors such as positional bias, length preferences, or scoring inconsistencies. Ensuring strong evaluation alignment allows organizations and researchers to deploy scalable, automated testing pipelines that serve as reliable and valid proxies for rigorous human review.

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Large Language Models are not Fair Evaluators

Large Language Models are not Fair Evaluators

Peiyi Wang, Lei Li, Liang Chen, Zefan Cai, Dawei Zhu, Binghuai Lin, Yunbo Cao, Lingpeng Kong, Qi Liu, Tianyu Liu, Zhifang Sui

OrganizationsPeking UniversityTencentUniversity of Hong Kong

Why you should read this

Reveals that using large language models as judges introduces severe positional bias that distorts model rankings, and provides effective calibration strategies to align automated evaluations with human judgments.

In this paper, we uncover a positional bias in the evaluation paradigm of adopting large language models (LLMs), e.g., GPT-4, as a referee to score and compare the quality of responses generated by candidate models. We find that the quality ranking of candidate responses can be easily hacked by simply altering their order of appearance in the context. This manipulation allows us to skew the evaluation result, making one model appear considerably superior to the other, e.g., Vicuna-13B could beat ChatGPT on 66 over 80 tested queries with ChatGPT as an evaluator. We propose a simple yet effective calibration framework to address our discovered positional bias. To evaluate the effectiveness of our framework, we manually annotate the “win/tie/lose” outcomes of responses from ChatGPT and Vicuna-13B in the Vicuna Benchmark’s question prompt. Extensive experiments demonstrate that our approach successfully alleviates evaluation bias, resulting in closer alignment with human judgments. To facilitate future research on more robust large language model comparison, we integrate the techniques in the paper into an easy-to-use toolkit FairEval, along with the human annotations 1.

Added

2026-09-28