Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
Abstract
Message from the Program Co-ChairsWelcome to EMNLP 2023, one of the most-attended conferences in the field of Natural Language Processing, held in "hybrid" mode this year serving both virtual and in-person participants in Singapore, where we have increased the number of Findings papers to be more inclusive and to showcase the diversity and quality of research in our field. Submission and AcceptanceEMNLP 2023 received 4,909 full paper submissions, the largest number to date.This number includes 134 ARR papers that were committed to EMNLP (see further discussion of ARR below).256 papers were desk rejected for various reasons (missing limitation section, anonymity policy, multiple submission policy or formatting violations), leaving us with submissions that were fully reviewed.This is a recordbreaking number of submissions, an increase of 719 submissions over last year.Based on the reviewers, area chairs and senior area chair comments, we have tried to keep the EMNLP 2023 main acceptance rates similar to previous events while increasing the number of Findings.We accepted 1,105 papers to the main conference including 9 nominated by the Computational Linguistics (CL) journal, and 49 by the Transactions of the Association of Computational Linguistics (TACL).Out of these, 234 are oral presentations and 871 poster presentations.We also accepted 1,060 papers for "Findings of EMNLP".Additionally, a total of 77 and 52 submissions were accepted to be presented in the Industry and Demo tracks, respectively.More statistics on the accepted papers can be found below.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Identifying when LLMs start to hallucinate or produce wrong results is difficult because correct answers are not known in advance and the internal mechanism of LLMs is not understood.
“Yet, identifying such timing is difficult because we neither know the correct answers in advance or understand the internal mechanism of LLMs (at least of today) [106]”
From Foundations of GenIR (2025) · cites this paper as [106]
Cites
Cho, Y., Rai, S., Ungar, L., Sedoc, J., & Guntuku, S. (2023) · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · doi:10.18653/v1/2023.emnlp-main.698 · Cited by 33 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Text-to-SQL Empowered by Large Language Models: A Benchmark Evaluation
Gao, D., Wang, H., Li, Y., Sun, X., Qian, Y., Ding, B., & Zhou, J. (2024) · Proceedings of the VLDB Endowment · doi:10.14778/3641204.3641221 · Cited by 342 · see citing works
Large language models for generative information extraction: a survey
Xu, D., Chen, W., Peng, W., Zhang, C., Xu, T., Zhao, X., Wu, X., Zheng, Y., Wang, Y., & Chen, E. (2024) · Frontiers of Computer Science · doi:10.1007/s11704-024-40555-y · Cited by 326 · see citing works
He, J., Treude, C., & Lo, D. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3712003 · Cited by 246 · see citing works
How Can Recommender Systems Benefit from Large Language Models: A Survey
Lin, J., Dai, X., Xi, Y., Liu, W., Chen, B., Zhang, H., Liu, Y., Wu, C., Li, X., Zhu, C., et al. (2025) · ACM Transactions on Information Systems · doi:10.1145/3678004 · Cited by 242 · see citing works
Wang, F., Zhang, Z., Zhang, X., Wu, Z., Mo, T., Lu, Q., Wang, W., Li, R., Xu, J., Tang, X., et al. (2025) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3768165 · Cited by 106 · see citing works
Chan, A., Chien, I., Moseley, E., Salman, S., Kaminer Bourland, S., Lamas, D., Walling, A. M., Tulsky, J. A., & Lindvall, C. (2019) · Palliative Medicine · doi:10.1177/0269216318810421 · Cited by 56 · see citing works
Patac, L. P., & Patac, A. V. (2025) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2025.101301 · Cited by 47 · see citing works
Clay, B., Bergman, H. I., Salim, S., Pergola, G., Shalhoub, J., & Davies, A. H. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.109808 · Cited by 41 · see citing works
Exploring the Capabilities of LLMs for Code-Change-Related Tasks
Fan, L., Liu, J., Liu, Z., Lo, D., Xia, X., & Li, S. (2025) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3709358 · Cited by 41 · see citing works
Fast State Restoration in LLM Serving with HCache
Gao, S., Chen, Y., & Shu, J. (2025) · Proceedings of the Twentieth European Conference on Computer Systems · doi:10.1145/3689031.3696072 · Cited by 24 · see citing works
The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models
Pradeep, R., Thakur, N., Upadhyay, S., Campos, D., Craswell, N., Soboroff, I., Dang, H. T., & Lin, J. (2025) · Proceedings of the 48th International ACM SIGIR Conference on Research and Development in Information Retrieval · doi:10.1145/3726302.3730090 · Cited by 14 · see citing works
Kazi, S., Khoja, S., & Daud, A. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2025.100756 · Cited by 9 · see citing works
Miao, Y., Wen, J., Luo, Y., & Li, J. (2026) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2025.106136 · Cited by 7 · see citing works
Gilani, K., Verket, M., Peters, C., Dumontier, M., Rocca, H.-P. B.-L., & Urovi, V. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110745 · Cited by 5 · see citing works
From RAG to Memory: Non-Parametric Continual Learning for Large Language Models
Gutiérrez, B. J., Shu, Y., Qi, W., Zhou, S., & Su, Y. (2025) · arXiv · doi:10.48550/arxiv.2502.14802 · Cited by 2 · see citing works
Ai, Q., Zhan, J., & Liu, Y. (2025) · arXiv · doi:10.48550/arxiv.2501.02842
C-SEO Bench: Does Conversational SEO Work?
Puerto, H., Gubri, M., Green, T., Oh, S. J., & Yun, S. (2025) · arXiv · doi:10.48550/arxiv.2506.11097
- Published
- 2023
- OpenAlex
- W2606555609
- Cited by
- 498
- References
- 1
- Citations by year
- 2025: 366 · 2024: 91 · 2023: 18 · 2022: 5 · 2021: 1 · 2020: 3 · 2019: 7 · 2018: 1 · 2017: 1 · 2016: 1 · 2015: 1 · 2014: 1
- Type
- paratext
- Language
- en
- Open access
- closed
- Funders
- Strong
- Ministry of Education, India
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Natural Language Processing Techniques
- Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
- Physical Sciences › Computer Science › Artificial Intelligence › Sentiment Analysis and Opinion Mining
- Keywords
- desk rejection, peer review
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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