Computer Science > Computation and Language
arXiv:2603.09403 (cs)
[Submitted on 10 Mar 2026]
Title:LLM as a Meta-Judge: Synthetic Data for NLP Evaluation Metric Validation
View a PDF of the paper titled LLM as a Meta-Judge: Synthetic Data for NLP Evaluation Metric Validation, by Luk\'a\v{s} Eigler and 2 other authors
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Abstract:Validating evaluation metrics for NLG typically relies on expensive and time-consuming human annotations, which predominantly exist only for English datasets. We propose \textit{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 \textit{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 will become publicly available upon paper acceptance.
| Comments: | |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2603.09403 [cs.CL] |
| (or arXiv:2603.09403v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2603.09403
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View a PDF of the paper titled LLM as a Meta-Judge: Synthetic Data for NLP Evaluation Metric Validation, by Luk\'a\v{s} Eigler and 2 other authors
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