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arXiv · 2609.15191

What Limits Us? Analyzing Self-Reported Limitations in NLP Research

Tawan Thaepprasit·Peeranuth Kehasukcharoen·Ding Wang·Remi Denton·Peerapon Vateekul·Piyawat Lertvittayakumjorn
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Abstract

Since late 2022, a Limitations section has become mandatory at many top-tier NLP conferences. The growing number of accepted papers at these venues has resulted in a vast corpus of self-reported limitations that cannot all be manually reviewed, yet remains systematically unanalyzed. Therefore, in this paper, we conduct a large-scale analysis of the Limitations sections from ACL and EMNLP papers published between 2020 and 2025 to understand what researchers disclose about their own work. To do so, we implement a novel human-AI framework for iterative hybrid qualitative coding. This framework enables us to investigate trends in self-reported limitations over time, their correlations with specific paper attributes, and the writing patterns that recur around these disclosures. Our findings offer a critical reflection on the diverse reported challenges as well as the self-reporting practices of researchers in the NLP community.

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Tawan Thaepprasit, Peeranuth Kehasukcharoen, Ding Wang, Remi Denton, Peerapon Vateekul, Piyawat Lertvittayakumjorn. 2026-09-14. What Limits Us? Analyzing Self-Reported Limitations in NLP Research. https://arxiv.org/abs/2609.15191

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