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This is a Quantitative Study study (also classified as: Literature Review).

October 11, 2025Open Access

The illusion of a perfect metric: Why evaluating AI's words is harder than it looks

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No single evaluation metric has proven to be a definitive solution for natural language generation.
Current metrics often capture specific text quality aspects, leading to task-dependent effectiveness.
Validation methods for these metrics are unstructured, contributing to inconsistency in human judgment correlations.
The latest metrics, such as large language models as evaluators, still face significant challenges in practical applications.

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Implication

This paper examines the reliability of evaluation metrics for AI's natural language generation, suggesting inconsistencies in human judgment correlations and highlighting limitations.

Cite This Study

Oliva et al. (2025) studied this question.

synapsesocial.com/papers/68e9b2e4ba7d64b6fc13317a https://doi.org/10.48550/arxiv.2508.13816

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Implication

This paper examines the reliability of evaluation metrics for AI's natural language generation, suggesting inconsistencies in human judgment correlations and highlighting limitations.

The illusion of a perfect metric: Why evaluating AI's words is harder than it looks

Key Points

Discussion

Implication

Cite This Study

The illusion of a perfect metric: Why evaluating AI's words is harder than it looks

Key Points

Discussion

Implication

Cite This Study

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