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

2026-09-14 08:11:57
Tawan Thaepprasit, Peeranuth Kehasukcharoen, Ding Wang, Remi Denton, Peerapon Vateekul, Piyawat Lertvittayakumjorn
arXiv_CL
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2609.15191

PDF

https://arxiv.org/pdf/2609.15191.pdf

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