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arXiv 2603.09403cs.CL
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LLM作为元评判者:用于NLP评估指标验证的合成数据

LLM as a Meta-Judge: Synthetic Data for NLP Evaluation Metric Validation

Faculty of Mathematics and Physics, Charles University(数学与物理系,查尔斯大学)

Lukáš Eigler, Jindřich Libovický, David Hurych

AI总结:

提出LLM作为元评判者框架,通过控制语义退化生成合成数据替代人工判断,验证NLG评估指标,在多语言问答中元相关性超过0.9。

摘要:

验证NLG的评估指标通常依赖于昂贵且耗时的人工标注,而这些标注主要仅存在于英语数据集。我们提出LLM作为元评判者,这是一个可扩展的框架,利用LLM通过控制真实数据的语义退化生成合成评估数据集,取代人工判断。我们使用 extit{元相关性}来验证我们的方法,衡量从合成数据得出的指标排名与标准人工基准之间的对齐程度。在机器翻译、问答和摘要上的实验表明,合成验证可作为人工判断的可靠代理,在多语言问答中实现超过0.9的元相关性,并证明在人工判断不可用或过于昂贵的情况下是一种可行的替代方案。我们的代码和数据将在论文被接受后公开。

英文摘要:

Validating evaluation metrics for NLG typically relies on expensive and time-consuming human annotations, which predominantly exist only for English datasets. We propose LLM as a Meta-Judge, a scalable framework that utilizes LLMs to generate synthetic evaluation datasets via controlled semantic degradation of real data, replacing human judgment. We validate our approach using meta-correlation, measuring the alignment between metric rankings derived from synthetic data and those from standard human benchmarks. Experiments across Machine Translation, Question Answering, and Summarization demonstrate that synthetic validation serves as a reliable proxy for human judgment, achieving meta-correlations exceeding 0.9 in multilingual QA and proves to be a viable alternative where human judgments are unavailable or too expensive to obtain. Our code and data are publicly available at https://github.com/eiglerl/meta-judge.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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