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This scoping review systematically catalogs 22 recurring methodological concerns in AI evaluation, revealing that many critiques have faded from recent literature despite lacking coverage in institutional checklists. By mapping these historical and current gaps, the paper offers a critical roadmap for researchers to address foundational weaknesses in how language models are assessed.
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What this isA model's reading of the article and related coverage, written for aiqbench. Not the publisher's words.

On Challenges in Evaluation Methodology: A Review

arXiv2 min read

arXiv:2604.25923v2 Announce Type: replace Abstract: Recent advances in language models have prompted a growing body of work that questions prevailing evaluation practices. However, many such critiques have already been discussed in natural language processing (NLP), a field with a long history of methodological reflection on evaluation. We conduct a scoping review spanning four decades and develop a taxonomy of 22 recurring concerns. For each concern, we examine how recently it was discussed in the reviewed literature and whether it receives explicit attention in institutional checklists, finding that some concerns have become less visible in literature and many lack coverage in these checklists. Our work provides an overview of the major concerns in evaluation methodology that have shaped the field and identifies potential targets for renewed investigation and field-level intervention by the research community.

Abstract:Recent advances in language models have prompted a growing body of work that questions prevailing evaluation practices. However, many such critiques have already been discussed in natural language processing (NLP), a field with a long history of methodological reflection on evaluation. We conduct a scoping review spanning four decades and develop a taxonomy of 22 recurring concerns. For each concern, we examine how recently it was discussed in the reviewed literature and whether it receives explicit attention in institutional checklists, finding that some concerns have become less visible in literature and many lack coverage in these checklists. Our work provides an overview of the major concerns in evaluation methodology that have shaped the field and identifies potential targets for renewed investigation and field-level intervention by the research community.

Computation and Language (cs.CL)

arXiv:2604.25923 [cs.CL]

(or arXiv:2604.25923v2 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2604.25923 Focus to learn more arXiv-issued DOI via DataCite

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Full text under the publisher's open licence · 221 words

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