May 29, 2023 · In this paper, we uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), eg, GPT-4, as a referee to score and compare ...
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 ...
This paper proposes a calibration framework with three simple yet effective strategies that successfully mitigates evaluation bias, resulting in closer ...
Dec 31, 2023 · Extensive experiments demonstrate that our approach successfully alleviates evaluation bias, resulting in closer alignment with human judgments.
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Sep 12, 2024 · We uncover a systematic bias in the evaluation paradigm of adopting large language models~(LLMs), e.g., GPT-4, as a referee to score the ...
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We have identified that positional bias can significantly impact the evaluation results of LLMs, making them unfair evaluators. In this section, we propose a ...
We reveal that LLMs exhibit severe positional bias, compromising their fairness as evaluators. We develop two simple yet effective strategies, namely Multiple ...
Sep 23, 2024 · However, these studies encounter three main limitations: 1. Lacking clear theoretical interpretability for bias definitions (e.g. ... ... Wang ...
Large Language Models are Diverse Role-Players for Summarization Evaluation · Computer Science, Linguistics. Natural Language Processing and Chinese Computing.
Aran Komatsuzaki on X: "Large Language Models are not Fair ...
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May 30, 2023 · Large Language Models are not Fair Evaluators - A bias in the evaluation of adopting LLMs, e.g., GPT-4, as a referee to score - Successfully ...
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