What Are We Measuring in NLG? A Meta-Analysis of Evaluation Trends 2020-2025

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Autori principali:	Yang, Jing, Feldhus, Nils, Mohtaj, Salar, Hennig, Leonhard, Wang, Qianli, Metheniti, Eleni, Hakimov, Sherzod, Jakob, Charlott, Solopova, Veronika, Rieck, Konrad, Schlangen, David, Möller, Sebastian, Schmitt, Vera
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Computation and Language
Accesso online:	Acceder al recurso
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author	Yang, Jing Feldhus, Nils Mohtaj, Salar Hennig, Leonhard Wang, Qianli Metheniti, Eleni Hakimov, Sherzod Jakob, Charlott Solopova, Veronika Rieck, Konrad Schlangen, David Möller, Sebastian Schmitt, Vera
author_facet	Yang, Jing Feldhus, Nils Mohtaj, Salar Hennig, Leonhard Wang, Qianli Metheniti, Eleni Hakimov, Sherzod Jakob, Charlott Solopova, Veronika Rieck, Konrad Schlangen, David Möller, Sebastian Schmitt, Vera
contents	As Natural Language Generation (NLG) dominates modern NLP, scalable evaluation remains a critical bottleneck. Consequently, LLM-as-a-judge (LaaJ) adoption has accelerated rapidly, appearing in more papers than human evaluation in 2025. This pivotal shift motivates a critical analysis of current evaluation practices. Overcoming the limits of rigid keyword filtering and manual review, we employ a multi-LLM information extraction pipeline to gather structured metadata from 14,171 papers across four major NLP conferences (2020-2025). Analyzing 3,334 filtered NLG papers, we identify three systemic challenges. (1) Metric inertia: despite the shift toward open-ended generation, legacy lexical metrics (BLEU, ROUGE) persist as primary indicators, typically used alongside rather than replaced by semantic alternatives. (2) Metric-criteria mapping problem: our paper-level co-occurrence data reveals that general-purpose automatic metrics are applied as broad proxies for quality, without specifying which dimension of text generation they are intended to evaluate. (3) Validation gap: LaaJ has grown rapidly without commensurate human validation (fewer than 8% of papers). Crucially, while LaaJ correlates with aggregate quality, alignment collapses on fine-grained criteria like fluency. To address these gaps, we distill our findings into a minimal Evaluation Checklist to guide metric selection, construct validity, and LaaJ deployment.
format	Preprint
id	arxiv_https___arxiv_org_abs_2601_07648
institution	arXiv
publishDate	2026
record_format	arxiv
spellingShingle	What Are We Measuring in NLG? A Meta-Analysis of Evaluation Trends 2020-2025 Yang, Jing Feldhus, Nils Mohtaj, Salar Hennig, Leonhard Wang, Qianli Metheniti, Eleni Hakimov, Sherzod Jakob, Charlott Solopova, Veronika Rieck, Konrad Schlangen, David Möller, Sebastian Schmitt, Vera Computation and Language As Natural Language Generation (NLG) dominates modern NLP, scalable evaluation remains a critical bottleneck. Consequently, LLM-as-a-judge (LaaJ) adoption has accelerated rapidly, appearing in more papers than human evaluation in 2025. This pivotal shift motivates a critical analysis of current evaluation practices. Overcoming the limits of rigid keyword filtering and manual review, we employ a multi-LLM information extraction pipeline to gather structured metadata from 14,171 papers across four major NLP conferences (2020-2025). Analyzing 3,334 filtered NLG papers, we identify three systemic challenges. (1) Metric inertia: despite the shift toward open-ended generation, legacy lexical metrics (BLEU, ROUGE) persist as primary indicators, typically used alongside rather than replaced by semantic alternatives. (2) Metric-criteria mapping problem: our paper-level co-occurrence data reveals that general-purpose automatic metrics are applied as broad proxies for quality, without specifying which dimension of text generation they are intended to evaluate. (3) Validation gap: LaaJ has grown rapidly without commensurate human validation (fewer than 8% of papers). Crucially, while LaaJ correlates with aggregate quality, alignment collapses on fine-grained criteria like fluency. To address these gaps, we distill our findings into a minimal Evaluation Checklist to guide metric selection, construct validity, and LaaJ deployment.
title	What Are We Measuring in NLG? A Meta-Analysis of Evaluation Trends 2020-2025
topic	Computation and Language
url	https://arxiv.org/abs/2601.07648

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