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

arXiv:2603.09403v1 Announce Type: new 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.

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Luk\'a\v{s} Eigler, Jind\v{r}ich Libovick\'y, David Hurych · · 1 min read · 31 views
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arXiv:2603.09403v1 Announce Type: new 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.

Executive Summary

The proposed framework, LLM as a Meta-Judge, utilizes large language models to generate synthetic evaluation datasets for natural language generation tasks, replacing the need for expensive human annotations. The approach is validated through meta-correlation analysis, demonstrating high alignment with human judgment in various tasks, including machine translation, question answering, and summarization. This scalable framework offers a reliable and cost-effective alternative for evaluation metric validation, particularly in multilingual settings where human judgments are scarce or unfeasible.

Key Points

  • ▸ LLM as a Meta-Judge framework generates synthetic evaluation datasets via controlled semantic degradation of real data
  • ▸ Meta-correlation analysis validates the approach, showing high alignment with human judgment
  • ▸ Experiments demonstrate the framework's effectiveness in multilingual question answering and other natural language generation tasks

Merits

Scalability and Cost-Effectiveness

The proposed framework offers a scalable and cost-effective solution for evaluation metric validation, reducing the need for expensive human annotations.

Demerits

Dependence on LLM Quality

The framework's performance is contingent upon the quality and accuracy of the large language models used to generate synthetic datasets.

Expert Commentary

The LLM as a Meta-Judge framework represents a significant step forward in addressing the longstanding challenge of evaluation metric validation in natural language generation. By leveraging large language models to generate synthetic datasets, the approach offers a scalable and cost-effective solution that can be applied across various tasks and languages. However, it is essential to carefully evaluate the quality and limitations of the LLMs used, as well as the potential biases introduced by the synthetic datasets. Further research is needed to fully explore the potential of this framework and its implications for the field.

Recommendations

  • ✓ Further evaluation of the framework's performance across a broader range of tasks and languages
  • ✓ Investigation into the potential biases and limitations of the synthetic datasets generated by the LLMs

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