CoChat
Sign Up
Cited by 2

The DOI matches its Crossref record.

The Nature of NLP: Analyzing Contributions in NLP Papers

Pramanick, Aniket, Hou, Yufang, Mohammad, Saif M., Gurevych, Iryna·2025·Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
DOI 10.18653/v1/2025.acl-long.1224
Open this paper in CoChat — free account

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.18653/v1/2025.acl-long.1224

Cites

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

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?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

No references could be resolved yet.

Research works printed but not linked: 74

  1. Titipat Achakulvisut, Chandra Bhagavatula, Daniel Acuna, and Konrad Kording. 2019. Claim extraction in biomedical publications using deep discourse model and transfer learning. arXiv preprint arXiv:1907.00962. · arXiv
  2. Gustavo Aguilar, Sudipta Kar, and Thamar Solorio. 2020. LinCE: A centralized benchmark for linguistic code-switching evaluation. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 1803-1813, Marseille, France. European Language Resources Association.
  3. Khalid Al Khatib, Tirthankar Ghosal, Yufang Hou, Anita de Waard, and Dayne Freitag. 2021. Argument mining for scholarly document processing: Taking stock and looking ahead. In Proceedings of the Second Workshop on Scholarly Document Processing, pages 56-65, Online. Association for Computational Linguistics. · DOI
  4. Ashton Anderson, Dan Jurafsky, and Daniel A. McFarland. 2012. Towards a computational history of the ACL: 1980-2008. In Proceedings of the ACL- 2012 Special Workshop on Rediscovering 50 Years of Discoveries, pages 13-21, Jeju Island, Korea. Association for Computational Linguistics.
  5. Roy Bar-Haim, Lilach Eden, Yoav Kantor, Roni Friedman, and Noam Slonim. 2021. Every bite is an experience: Key Point Analysis of business reviews. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 3376-3386, Online. Association for Computational Linguistics. · DOI
  6. Iz Beltagy, Kyle Lo, and Arman Cohan. 2019. SciBERT: A pretrained language model for scientific text. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 3615-3620, Hong Kong, China. Association for Computational Linguistics. · DOI · Paper page
  7. Ronald J. Brachman. 1979. Taxonomy, descriptions, and individuals in natural language understanding. In 17th Annual Meeting of the Association for Computational Linguistics, pages 33-37, La Jolla, California, USA. Association for Computational Linguistics. · DOI
  8. Peter F. Brown, Stephen A. Della Pietra, Vincent J. Della Pietra, and Robert L. Mercer. 1993. The mathematics of statistical machine translation: Parameter estimation. Computational Linguistics, 19(2):263-311.
  9. Sébastien Bubeck, Varun Chandrasekaran, Ronen Eldan, Johannes Gehrke, Eric Horvitz, Ece Kamar, Peter Lee, Yin Tat Lee, Yuanzhi Li, Scott Lundberg, et al. 2024. Sparks of artificial general intelligence: Early experiments with GPT-4. arXiv preprint arXiv:2303.12712, 10. · arXiv
  10. Chaomei Chen and Min Song. 2019. Visualizing a field of research: A methodology of systematic scientometric reviews. PLOS ONE, 14(10):1-25. · DOI
  11. Sihao Chen, Daniel Khashabi, Wenpeng Yin, Chris Callison-Burch, and Dan Roth. 2019. Seeing things from a different angle:discovering diverse perspectives about claims. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 542- 557, Minneapolis, Minnesota. Association for Computational Linguistics. · DOI · Paper page
  12. Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Yunxuan Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Alex Castro-Ros, Marie Pellat, Kevin Robinson, Dasha Valter, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei. 2024. Scaling instruction- finetuned language models. Journal of Machine Learning Research, 25(70):1-53.
  13. Herbert H Clark and Michael F Schober. 1992. Asking questions and influencing answers. Contemporary Sociology, 22(1):15-48.
  14. Pradeep Dasigi, Kyle Lo, Iz Beltagy, Arman Cohan, Noah A. Smith, and Matt Gardner. 2021. A dataset of information-seeking questions and answers anchored in research papers. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4599-4610, Online. Association for Computational Linguistics. · DOI · Paper page
  15. Yuntian Deng, Volodymyr Kuleshov, and Alexander Rush. 2022. Model criticism for long-form text generation. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 11887-11912, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics. · DOI
  16. Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers), pages 4171-4186, Minneapolis, Minnesota. Association for Computational Linguistics. · DOI · Paper page
  17. Cailing Dong and Ulrich Schäfer. 2011. Ensemble- style self-training on citation classification. In Proceedings of 5th International Joint Conference on Natural Language Processing, pages 623-631, Chiang Mai, Thailand. Asian Federation of Natural Language Processing.
  18. Jennifer D'Souza and Sören Auer. 2020. NLPContributions: An annotation scheme for machine reading of scholarly contributions in natural language processing literature. In Proceedings of the 3rd Workshop on Extraction and Evaluation of Knowledge Entities from Scientific Documents (EEKE 2022), Germany and Online. CEUR-WS.
  19. Jennifer D'Souza, Sören Auer, and Ted Pedersen. 2021. SemEval-2021 task 11: NLPContributionGraph -structuring scholarly NLP contributions for a research knowledge graph. In Proceedings of the 15th International Workshop on Semantic Evaluation (SemEval-2021), pages 364-376, Online. Association for Computational Linguistics. · DOI
  20. Greg Durrett, Jonathan K. Kummerfeld, Taylor Berg-Kirkpatrick, Rebecca Portnoff, Sadia Afroz, Damon McCoy, Kirill Levchenko, and Vern Paxson. 2017. Identifying products in online cybercrime marketplaces: A dataset for fine- grained domain adaptation. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 2598- 2607, Copenhagen, Denmark. Association for Computational Linguistics. · DOI
  21. Sauleh Eetemadi and Kristina Toutanova. 2014. Asymmetric features of human generated translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 159-164, Doha, Qatar. Association for Computational Linguistics. · DOI
  22. Raymond Fok, Luca Soldaini, Cassidy Trier, Erin Bransom, Kelsey MacMillan, Evie Cheng, Hita Kambhamettu, Jonathan Bragg, Kyle Lo, Marti A. Hearst, Andrew Head, and Daniel S. Weld. 2024. Accelerating scientific paper skimming with augmented intelligence through customizable faceted highlights. ACM Trans. Interact. Intell. Syst., 14(4). · DOI
  23. Roni Friedman, Lena Dankin, Yufang Hou, Ranit Aharonov, Yoav Katz, and Noam Slonim. 2021. Overview of the 2021 key point analysis shared task. In Proceedings of the 8th Workshop on Argument Mining, pages 154-164, Punta Cana, Dominican Republic. Association for Computational Linguistics. · DOI
  24. Mark Garzone and Robert E Mercer. 2000. Towards an automated citation classifier. In Advances in Artificial Intelligence: 13th Biennial Conference of the Canadian Society for Computational Studies of Intelligence, AI 2000 Montéal, Quebec, Canada, May 14-17, 2000 Proceedings 13, pages 337-346. · DOI · Paper page
  25. Yu Gu, Robert Tinn, Hao Cheng, Michael Lucas, Naoto Usuyama, Xiaodong Liu, Tristan Naumann, Jianfeng Gao, and Hoifung Poon. 2021. Domain- specific language model pretraining for biomedical natural language processing. ACM Trans. Comput. Healthcare, 3(1). · DOI · Paper page
  26. Komal Gupta, Ammaar Ahmad, Tirthankar Ghosal, and Asif Ekbal. 2021. ContriSci: a bert-based multitasking deep neural architecture to identify contribution statements from research papers. In Proceedings of the 23rd International Conference on Asia-Pacific Digital Libraries: Towards Open and Trustworthy Digital Societies, ICADL 2021, pages 436-452, Virtual Event. Springer. · DOI
  27. Komal Gupta, Ammaar Ahmad, Tirthankar Ghosal, and Asif Ekbal. 2024. A BERT-based sequential deep neural architecture to identify contribution statements and extract phrases for triplets from scientific publications. In International Journal on Digital Libraries, volume 25, pages 1-28. Springer. · DOI
  28. David Hall, Daniel Jurafsky, and Christopher D. Manning. 2008. Studying the history of ideas using topic models. In Proceedings of the 2008 Conference on Empirical Methods in Natural Language Processing, pages 363-371, Honolulu, Hawaii. Association for Computational Linguistics.
  29. Annette Herskovits. 1980. On the spatial uses of prepositions. In 18th Annual Meeting of the Association for Computational Linguistics, pages 1- 5, Philadelphia, Pennsylvania, USA. Association for Computational Linguistics. · DOI
  30. Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, and Debasis Ganguly. 2019. Identification of tasks, datasets, evaluation metrics, and numeric scores for scientific leaderboards construction. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 5203-5213, Florence, Italy. Association for Computational Linguistics. · DOI
  31. Yufang Hou, Charles Jochim, Martin Gleize, Francesca Bonin, and Debasis Ganguly. 2021. TDMSci: A specialized corpus for scientific literature entity tagging of tasks datasets and metrics. In Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume, pages 707-714, Online. Association for Computational Linguistics. · DOI
  32. Marcio Inácio and Thiago Pardo. 2021. Semantic- based opinion summarization. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2021), pages 619-628, Held Online. INCOMA Ltd.
  33. David Jurgens, Srijan Kumar, Raine Hoover, Dan McFarland, and Dan Jurafsky. 2018a. Measuring the evolution of a scientific field through citation frames. Transactions of the Association for Computational Linguistics, 6:391-406. · DOI · Paper page
  34. David Jurgens, Srijan Kumar, Raine Hoover, Dan McFarland, and Dan Jurafsky. 2018b. Measuring the evolution of a scientific field through citation frames. Transactions of the Association for Computational Linguistics, 6:391-406. · DOI · Paper page
  35. Jan-Christoph Klie, Richard Eckart de Castilho, and Iryna Gurevych. 2024. Analyzing dataset annotation quality management in the wild. Computational Linguistics, 50(3):817-866. · DOI
  36. Anne Lauscher, Brandon Ko, Bailey Kuehl, Sophie Johnson, Arman Cohan, David Jurgens, and Kyle Lo. 2022. MultiCite: Modeling realistic citations requires moving beyond the single-sentence single- label setting. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 1875-1889, Seattle, United States. Association for Computational Linguistics. · DOI
  37. Michael Lebowitz. 1979. Reading with a purpose. In 17th Annual Meeting of the Association for Computational Linguistics, pages 59-63, La Jolla, California, USA. Association for Computational Linguistics. · DOI
  38. Sha Li, Chi Han, Pengfei Yu, Carl Edwards, Manling Li, Xingyao Wang, Yi Fung, Charles Yu, Joel Tetreault, Eduard Hovy, and Heng Ji. 2023. Defining a new NLP playground. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 11932-11951, Singapore. Association for Computational Linguistics. · DOI
  39. Maria Liakata, Simone Teufel, Advaith Siddharthan, and Colin Batchelor. 2010. Corpora for the conceptualisation and zoning of scientific papers. In Proceedings of the Seventh International Conference on Language Resources and Evaluation (LREC'10), Valletta, Malta. European Language Resources Association (ELRA).
  40. Kyle Lo, Lucy Lu Wang, Mark Neumann, Rodney Kinney, and Daniel Weld. 2020. S2ORC: The semantic scholar open research corpus. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 4969-4983, Online. Association for Computational Linguistics. · DOI · Paper page
  41. Mitchell P. Marcus, Beatrice Santorini, and Mary Ann Marcinkiewicz. 1993. Building a large annotated corpus of English: The Penn Treebank. Computational Linguistics, 19(2):313-330.
  42. ML Menéndez, JA Pardo, L Pardo, and MC Pardo. 1997. The jensen-shannon divergence. Journal of the Franklin Institute, 334(2):307-318.
  43. Filipe Mesquita, Jordan Schmidek, and Denilson Barbosa. 2013. Effectiveness and efficiency of open relation extraction. In Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing, pages 447-457, Seattle, Washington, USA. Association for Computational Linguistics.
  44. John Mingers and Loet Leydesdorff. 2015. A review of theory and practice in scientometrics. European Journal of Operational Research, 246(1):1-19. · DOI · Paper page
  45. Saif M. Mohammad. 2020a. Examining citations of natural language processing literature. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 5199-5209, Online. Association for Computational Linguistics. · DOI
  46. Saif M. Mohammad. 2020b. NLP scholar: A dataset for examining the state of NLP research. In Proceedings of the Twelfth Language Resources and Evaluation Conference, pages 868-877, Marseille, France. European Language Resources Association.
  47. Benjamin Newman, Yoonjoo Lee, Aakanksha Naik, Pao Siangliulue, Raymond Fok, Juho Kim, Daniel S Weld, Joseph Chee Chang, and Kyle Lo. 2024. ArxivDIGESTables: Synthesizing scientific literature into tables using language models. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 9612-9631, Miami, Florida, USA. Association for Computational Linguistics. · DOI · Paper page
  48. Ali Omrani, Alireza Salkhordeh Ziabari, Charles Yu, Preni Golazizian, Brendan Kennedy, Mohammad Atari, Heng Ji, and Morteza Dehghani. 2023. Social- group-agnostic bias mitigation via the stereotype content model. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 4123-4139, Toronto, Canada. Association for Computational Linguistics. · DOI
  49. Charles Oppenheim and Susan P Renn. 1978. Highly cited old papers and the reasons why they continue to be cited. Journal of the American Society for Information Science, 29(5):225-231. · DOI · Paper page
  50. Maria Leonor Pacheco, Tunazzina Islam, Monal Mahajan, Andrey Shor, Ming Yin, Lyle Ungar, and Dan Goldwasser. 2022. A holistic framework for analyzing the COVID-19 vaccine debate. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 5821-5839, Seattle, United States. Association for Computational Linguistics. · DOI
  51. Nanyun Peng and Mark Dredze. 2015. Named entity recognition for Chinese social media with jointly trained embeddings. In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing, pages 548-554, Lisbon, Portugal. Association for Computational Linguistics. · DOI
  52. Vinodkumar Prabhakaran, William L. Hamilton, Dan McFarland, and Dan Jurafsky. 2016. Predicting the rise and fall of scientific topics from trends in their rhetorical framing. In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 1170-1180, Berlin, Germany. Association for Computational Linguistics. · DOI
  53. Aniket Pramanick, Yufang Hou, Saif Mohammad, and Iryna Gurevych. 2023. A diachronic analysis of paradigm shifts in NLP research: When, how, and why? In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 2312-2326, Singapore. Association for Computational Linguistics. · DOI
  54. Jesse Read, Bernhard Pfahringer, Geoff Holmes, and Eibe Frank. 2009. Classifier chains for multi-label classification. In Machine Learning and Knowledge Discovery in Databases, pages 254-269, Berlin, Heidelberg. Springer Berlin Heidelberg. · DOI
  55. Furkan Şahinuç, Thy Thy Tran, Yulia Grishina, Yufang Hou, Bei Chen, and Iryna Gurevych. 2024. Efficient performance tracking: Leveraging large language models for automated construction of scientific leaderboards. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 7963-7977, Miami, Florida, USA. Association for Computational Linguistics. · DOI
  56. Said A Salloum, Mostafa Al-Emran, Azza Abdel Monem, and Khaled Shaalan. 2017. A survey of text mining in social media: Facebook and twitter perspectives. Advances in Science, Technology and Engineering Systems, 2(1):127-133.
  57. Dudley Shapere. 1964. The structure of scientific revolutions. The Philosophical Review, 73(3):383- 394.
  58. Daniel Sosa, Malavika Suresh, Christopher Potts, and Russ Altman. 2023. Detecting contradictory COVID- 19 drug efficacy claims from biomedical literature. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 694-713, Toronto, Canada. Association for Computational Linguistics. · DOI
  59. Mary Elizabeth Stevens and Vincent Edward Giuliano. 1965. Statistical association methods for mechanized documentation: Symposium proceedings, volume 269. US Government Printing Office.
  60. Chenhao Tan, Dallas Card, and Noah A. Smith. 2017. Friendships, rivalries, and trysts: Characterizing relations between ideas in texts. In Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 773-783, Vancouver, Canada. Association for Computational Linguistics. · DOI
  61. Daniela Teodorescu and Saif Mohammad. 2023. Evaluating emotion arcs across languages: Bridging the global divide in sentiment analysis. In Findings of the Association for Computational Linguistics: · DOI
  62. Simone Teufel, Advaith Siddharthan, and Dan Tidhar. 2006. Automatic classification of citation function. In Proceedings of the 2006 Conference on Empirical Methods in Natural Language Processing, pages 103-110, Sydney, Australia. Association for Computational Linguistics.
  63. Simone Teufel et al. 1999. Argumentative zoning: Information extraction from scientific text. Ph.D. thesis, University of Edinburgh.
  64. Alexandra N. Uma, Tommaso Fornaciari, Dirk Hovy, Silviu Paun, Barbara Plank, and Massimo Poesio. 2022. Learning from disagreement: A survey. J. Artif. Int. Res., 72:1385-1470. · DOI
  65. Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Ł ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in Neural Information Processing Systems, volume 30.
  66. Jan Philip Wahle, Terry Ruas, Saif Mohammad, and Bela Gipp. 2022. D3: A massive dataset of scholarly metadata for analyzing the state of computer science research. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 2642-2651, Marseille, France. European Language Resources Association.
  67. Yidong Wang, Qi Guo, Wenjin Yao, Hongbo Zhang, Xin Zhang, Zhen Wu, Meishan Zhang, Xinyu Dai, Min Zhang, Qingsong Wen, Wei Ye, Shikun Zhang, and Yue Zhang. 2024. Autosurvey: Large language models can automatically write surveys. In Advances in Neural Information Processing Systems, volume 37, pages 115119-115145.
  68. Jason Wei, Maarten Bosma, Vincent Zhao, Kelvin Guu, Adams Wei Yu, Brian Lester, Nan Du, Andrew M. Dai, and Quoc V Le. 2022a. Finetuned language models are zero-shot learners. In International Conference on Learning Representations.
  69. Jason Wei, Yi Tay, Rishi Bommasani, Colin Raffel, Barret Zoph, Sebastian Borgeaud, Dani Yogatama, Maarten Bosma, Denny Zhou, Donald Metzler, Ed H. Chi, Tatsunori Hashimoto, Oriol Vinyals, Percy Liang, Jeff Dean, and William Fedus. 2022b. Emergent abilities of large language models. Transactions on Machine Learning Research. Survey Certification.
  70. An Yang and Sujian Li. 2018. SciDTB: Discourse dependency TreeBank for scientific abstracts. In Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 444-449, Melbourne, Australia. Association for Computational Linguistics. · DOI
  71. Chao Zhao, Tenghao Huang, Somnath Basu Roy Chowdhury, Muthu Kumar Chandrasekaran, Kathleen McKeown, and Snigdha Chaturvedi. 2022. Read top news first: A document reordering approach for multi-document news summarization. In Findings of the Association for Computational Linguistics: ACL 2022, pages 613-621, Dublin, Ireland. Association for Computational Linguistics. · DOI
  72. Jiayi Zhao, Xipeng Qiu, Shu Zhang, Feng Ji, and Xuanjing Huang. 2012. Part-of-speech tagging for Chinese-English mixed texts with dynamic features. In Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, pages 1379-1388, Jeju Island, Korea. Association for Computational Linguistics.
  73. Yuxiang Zhou, Lejian Liao, Yang Gao, Zhanming Jie, and Wei Lu. 2021. To be closer: Learning to link up aspects with opinions. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 3899-3909, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics. · DOI
  74. Liu Zhuang, Lin Wayne, Shi Ya, and Zhao Jun. 2021. A robustly optimized BERT pre-training approach with post-training. In Proceedings of the 20th Chinese National Conference on Computational Linguistics, pages 1218-1227, Huhhot, China. Chinese Information Processing Society of China.

Cited by

Sorted by citations

See all 2

Metadata

Authors
Pramanick, A., Hou, Y., Mohammad, S. M., & Gurevych, I.
Venue
Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Publisher
Association for Computational Linguistics
Published
2025
Pages
25169-25191
DOI
10.18653/v1/2025.acl-long.1224
Cited by
2
References
0

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 ›

Are you an author, or do you see a problem with this page? Request a review