G-Eval uses GPT-4 with chain-of-thought and form-filling to reach 0.514 Spearman correlation with humans on summarization, beating prior NLG metrics while noting a bias toward LLM outputs.
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Contrastive decoding reduces score-range sensitivity in LLM judges for summarization and raises average Spearman correlation with human ratings by up to 11.7 percent across different score ranges.
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G-Eval: NLG Evaluation using GPT-4 with Better Human Alignment
G-Eval uses GPT-4 with chain-of-thought and form-filling to reach 0.514 Spearman correlation with humans on summarization, beating prior NLG metrics while noting a bias toward LLM outputs.
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Contrastive Decoding Mitigates Score Range Bias in LLM-as-a-Judge
Contrastive decoding reduces score-range sensitivity in LLM judges for summarization and raises average Spearman correlation with human ratings by up to 11.7 percent across different score ranges.