pith. sign in

arxiv: 2607.02416 · v1 · pith:EJX5Y5NEnew · submitted 2026-07-02 · 💻 cs.CL

The Future of NLP may not be at NLP Conferences: Scholarly Migration Patterns in Natural Language Processing

David Jurgens This is my paper

Pith reviewed 2026-07-03 14:26 UTC · model grok-4.3

classification 💻 cs.CL
keywords NLPmachine learningscholarly publishingvenue migrationcitation analysislarge language modelsacademic conferencesauthor behavior
0
0 comments X

The pith

NLP research is migrating from core disciplinary conferences to general machine learning venues.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper tracks publication patterns for NLP work from 2010 to 2026 to test whether the center of gravity is moving away from traditional venues. Established authors reduced their share at main *ACL conference tracks by 19.2 percentage points while increasing presence in newer Findings tracks and general ML venues. New authors who produce multiple first-author NLP papers now direct a larger portion of their output to general ML venues, rising from 5 percent to 21 percent. Causal inference links this pattern to a citation premium available at ML venues. These shifts matter because venue choice shapes visibility, collaboration, and the boundaries of the field.

Core claim

Comparing the pre- and post-LLM eras, established authors lost 19.2pp of share at flagship *ACL main-conference tracks while gaining 14.8pp in the newer Findings tracks, and general ML venues rose 8.6pp, even when adjusting for parallel growth in the fields. Among newer authors who debut with at least three first-author NLP-topic papers, the share whose work appears mostly at *ACL venues fell from 84% (2019) to 74% (2024), while the share appearing mostly at general ML venues rose from 5% to 21%. Using causal inference techniques, the paper estimates that these general ML venues confer a significant citation premium, which influences venue selection.

What carries the argument

Venue share calculations across *ACL main tracks, Findings, and general ML categories, combined with causal inference on citation effects.

If this is right

  • Established authors are directing a growing fraction of their NLP work to Findings tracks and general ML venues.
  • New authors who enter with multiple NLP papers show a rising preference for publishing mostly in general ML venues.
  • The citation premium at general ML venues shapes author decisions on where to submit.
  • The disciplinary center of NLP research is moving as boundaries with general machine learning blur.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Continued migration could alter how NLP-specific conferences maintain submission volume and review standards.
  • Over time, citation-based evaluation systems may increasingly favor general ML venues for work that originated in NLP.
  • The trend raises questions about whether NLP research will retain distinct identity or become absorbed into broader ML publication norms.

Load-bearing premise

Papers can be accurately and consistently classified as NLP-topic versus general ML, with venue categories remaining cleanly separated without substantial overlap or mislabeling.

What would settle it

A re-run of the share calculations and causal models on the same corpus using an independent topic classifier or alternative venue groupings that eliminates the reported 19.2pp and 16pp shifts.

Figures

Figures reproduced from arXiv: 2607.02416 by David Jurgens.

Figure 2
Figure 2. Figure 2: Mean within-author change in renormalized venue-mix share (percentage points) from 2015–20 to 2021–26, per venue family, with 95% cluster-robust CIs; any-authorship vs. first-or-last-author. Full coefficients in Appendix [PITH_FULL_IMAGE:figures/full_fig_p005_2.png] view at source ↗
Figure 4
Figure 4. Figure 4: Median per-author cosine similarity of pre-vs [PITH_FULL_IMAGE:figures/full_fig_p007_4.png] view at source ↗
Figure 3
Figure 3. Figure 3: Results from the Oaxaca–Blinder decompo [PITH_FULL_IMAGE:figures/full_fig_p007_3.png] view at source ↗
Figure 5
Figure 5. Figure 5: Per-venue/year paper coverage on a log color scale. Coverage is dense across the 2015–2026 window for [PITH_FULL_IMAGE:figures/full_fig_p017_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Share of each topic cluster’s papers that appear at *ACL venues, pooled into two-year periods (2016–2025; [PITH_FULL_IMAGE:figures/full_fig_p019_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Citation premium (vs. ACL/NLP) by venue category under the observable-controls and author-fixed￾effects specifications. The GENERAL-ML premium is large and positive under both; the within-author esti￾mate is close to the between-author one, indicating little author-selection bias. 19 [PITH_FULL_IMAGE:figures/full_fig_p019_7.png] view at source ↗
read the original abstract

Natural Language Processing (NLP) has traditionally been published in its core disciplinary venues like ACL. However, advances in Large Language Models (LLMs) has led to a blurring of the disciplinary lines between NLP and general Machine Learning (ML), with authors regularly publishing in venues from both fields. Here, we ask whether the disciplinary center of gravity is shifting. Using NLP research published from 2010 to 2026 and studies of both established and new authors, we find that a migration is taking place. First, comparing the pre- and post-LLM eras, established authors lost 19.2pp of share at flagship *ACL main-conference tracks while gaining 14.8pp in the newer Findings tracks, and general ML venues rose 8.6pp, even when adjusting for parallel growth in the fields. Second, among newer authors who debut with at least three first-author NLP-topic papers, the share whose work appears mostly at *ACL venues fell from 84% (2019) to 74% (2024), while the share appearing mostly at general ML venues rose from 5% to 21%. Using causal inference techniques, we estimate that these general ML venues confer a significant citation premium, which influences venue selection. Together, these results point to a significant shift in where NLP research is published.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

3 major / 2 minor

Summary. The paper claims that NLP research is migrating from core *ACL venues to general ML venues in the post-LLM era. Using data from 2010–2026, it reports that established authors lost 19.2pp share at flagship *ACL main tracks (gaining 14.8pp in Findings and 8.6pp in general ML venues) even after adjusting for field growth; among new authors debuting with ≥3 first-author NLP-topic papers, *ACL dominance fell from 84% (2019) to 74% (2024) while ML dominance rose from 5% to 21%. Causal inference is used to estimate a citation premium for general ML venues that influences venue choice.

Significance. If the classification procedures and causal estimates prove robust, the findings would be significant for documenting real shifts in NLP publishing patterns and the blurring of disciplinary boundaries. The large temporal scope and dual analysis of established versus new authors are strengths; however, the absence of inspectable data-construction steps limits immediate impact.

major comments (3)
  1. [Abstract / Methods] Abstract and Methods (assumed §3–4): All headline share shifts (19.2pp, 14.8pp, 8.6pp; 84%→74%, 5%→21%) rest on a binary or multi-class labeling of papers as “NLP-topic.” The manuscript supplies no description of the classifier (keywords, embeddings, heuristics), training data, validation metrics, or tests for temporal stability across the 2010–2026 window, especially around the 2022–2023 LLM inflection. This is load-bearing for every reported percentage and the “adjusting for parallel growth” claim.
  2. [Abstract] Abstract: The causal-inference step that attributes a “significant citation premium” to general ML venues is presented without naming the estimator, identifying assumptions, listing covariates, or reporting robustness checks. Because venue selection is the outcome variable, the same classification rule used for the share calculations is inherited, amplifying the need for explicit methodological detail.
  3. [Abstract] Abstract: The statement that results hold “even when adjusting for parallel growth in the fields” is asserted without describing the adjustment procedure, the growth metric employed, or the data sources used to construct the counterfactual. This adjustment is central to the migration interpretation.
minor comments (2)
  1. [Abstract] Clarify the exact set of venues included in “flagship *ACL main-conference tracks” versus “Findings tracks” and list any overlap or reclassification rules applied over time.
  2. [Abstract] The phrase “newer authors who debut with at least three first-author NLP-topic papers” should be accompanied by the precise definition of “debut” year and the window used to count the three papers.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for highlighting the need for greater methodological transparency. We have revised the manuscript to expand the abstract and add dedicated Methods subsections with full details on classification, causal inference, and growth adjustment. Point-by-point responses below.

read point-by-point responses

  1. Referee: [Abstract / Methods] All headline share shifts rest on a binary or multi-class labeling of papers as “NLP-topic.” The manuscript supplies no description of the classifier (keywords, embeddings, heuristics), training data, validation metrics, or tests for temporal stability across the 2010–2026 window, especially around the 2022–2023 LLM inflection.

    Authors: We agree the classifier is load-bearing and should be explicit. The revised manuscript adds §3.1 describing a hybrid rule-based classifier: arXiv cs.CL category filter plus ACL anthology keyword heuristics, validated on a 1,000-paper hand-labeled set (precision 0.93, recall 0.89). Temporal stability tests (pre/post-2022 splits) show <3pp F1 drop. This underpins all reported shares and the growth adjustment. revision: yes

  2. Referee: [Abstract] The causal-inference step that attributes a “significant citation premium” to general ML venues is presented without naming the estimator, identifying assumptions, listing covariates, or reporting robustness checks.

    Authors: The estimator is two-stage least squares with lagged venue-level citation rates as instrument for venue choice. Assumptions (relevance, exclusion restriction) are stated with first-stage diagnostics (F>12). Covariates: author h-index, paper length, year fixed effects. Robustness includes alternative instruments and propensity-score matching; all reported in new §4.3 and appendix. Abstract now names the estimator. revision: yes

  3. Referee: [Abstract] The statement that results hold “even when adjusting for parallel growth in the fields” is asserted without describing the adjustment procedure, the growth metric employed, or the data sources used to construct the counterfactual.

    Authors: The adjustment normalizes venue shares by annual publication volume growth in arXiv cs.CL vs. cs.LG (sourced from arXiv bulk metadata). Counterfactual holds venue proportions fixed while scaling by field size. Procedure, formula, and sensitivity to Google Scholar counts now appear in §4.2; results remain directionally unchanged. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical shares and causal estimates are computed from external data

full rationale

The paper computes venue-share shifts and citation-premium estimates directly from publication records spanning 2010–2026. No equation or result is defined in terms of itself, no fitted parameter is relabeled as a prediction, and no load-bearing premise reduces to a self-citation. The NLP-topic classification and venue labels function as measurement inputs rather than outputs that loop back to justify the reported percentages.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The abstract depends on two unverified domain assumptions about data categorization and causal identification; no free parameters or invented entities are introduced in the provided text.

axioms (2)
  • domain assumption NLP papers can be reliably identified and classified by topic and venue over the 2010-2026 period
    Required to compute the reported share changes for established and new authors.
  • domain assumption Causal inference techniques can isolate the effect of venue on citations from confounding factors
    Invoked to conclude that the citation premium influences venue selection.

pith-pipeline@v0.9.1-grok · 5767 in / 1455 out tokens · 31382 ms · 2026-07-03T14:26:33.420291+00:00 · methodology

discussion (0)

Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.

Reference graph

Works this paper leans on

83 extracted references · 29 canonical work pages · 2 internal anchors

  1. [1]

    Towards a Computational History of the ACL : 1980-2008

    Anderson, Ashton and Jurafsky, Dan and McFarland, Daniel A. Towards a Computational History of the ACL : 1980-2008. Proceedings of the ACL -2012 Special Workshop on Rediscovering 50 Years of Discoveries. 2012

    1980

  2. [2]

    International Conference on Learning Representations (ICLR) , year=

    ReAct: Synergizing Reasoning and Acting in Language Models , author=. International Conference on Learning Representations (ICLR) , year=

  3. [3]

    Finetuned Language Models are Zero-Shot Learners , author=

  4. [4]

    , author=

    Lora: Low-rank adaptation of large language models. , author=. Iclr , volume=

  5. [5]

    Advances in neural information processing systems , volume=

    Language models are few-shot learners , author=. Advances in neural information processing systems , volume=

  6. [6]

    Advances in neural information processing systems , volume=

    Training language models to follow instructions with human feedback , author=. Advances in neural information processing systems , volume=

  7. [7]

    Advances in neural information processing systems , volume=

    Chain-of-thought prompting elicits reasoning in large language models , author=. Advances in neural information processing systems , volume=

  8. [8]

    PLoS one , volume=

    Where should I send it? Optimizing the submission decision process , author=. PLoS one , volume=. 2015 , publisher=

    2015

  9. [9]

    Publications , volume=

    What motivates authors of scholarly articles? The importance of journal attributes and potential audience on publication choice , author=. Publications , volume=. 2016 , publisher=

    2016

  10. [10]

    Proceedings of the 36th International Conference on Neural Information Processing Systems , pages=

    Training compute-optimal large language models , author=. Proceedings of the 36th International Conference on Neural Information Processing Systems , pages=

  11. [11]

    International economic review , pages=

    Male-female wage differentials in urban labor markets , author=. International economic review , pages=. 1973 , publisher=

    1973

  12. [12]

    Journal of Human resources , pages=

    Wage discrimination: reduced form and structural estimates , author=. Journal of Human resources , pages=. 1973 , publisher=

    1973

  13. [13]

    Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    Scirepeval: A multi-format benchmark for scientific document representations , author=. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing , pages=

    2023

  14. [14]

    Transactions of the Association for Computational Linguistics , volume=

    Measuring the evolution of a scientific field through citation frames , author=. Transactions of the Association for Computational Linguistics , volume=

  15. [15]

    The ACL A nthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics

    Bird, Steven and Dale, Robert and Dorr, Bonnie and Gibson, Bryan and Joseph, Mark and Kan, Min-Yen and Lee, Dongwon and Powley, Brett and Radev, Dragomir and Tan, Yee Fan. The ACL A nthology Reference Corpus: A Reference Dataset for Bibliographic Research in Computational Linguistics. Proceedings of the Sixth International Conference on Language Resources...

    2008

  16. [16]

    and Muthukrishnan, Pradeep and Qazvinian, Vahed and Abu-Jbara, Amjad

    Radev, Dragomir R. and Muthukrishnan, Pradeep and Qazvinian, Vahed and Abu-Jbara, Amjad. The ACL Anthology Network Corpus. Language Resources and Evaluation. 2013. doi:10.1007/s10579-012-9211-2

    work page doi:10.1007/s10579-012-9211-2 2013

  17. [17]

    The glass ceiling in NLP

    Schluter, Natalie. The glass ceiling in NLP. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing. 2018. doi:10.18653/v1/D18-1301

    work page doi:10.18653/v1/d18-1301 2018

  18. [18]

    NLP Scholar: An Interactive Visual Explorer for Natural Language Processing Literature

    Mohammad, Saif M. NLP Scholar: An Interactive Visual Explorer for Natural Language Processing Literature. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations. 2020. doi:10.18653/v1/2020.acl-demos.27

    work page doi:10.18653/v1/2020.acl-demos.27 2020

  19. [19]

    NLP Scholar: A Dataset for Examining the State of NLP Research

    Mohammad, Saif M. NLP Scholar: A Dataset for Examining the State of NLP Research. Proceedings of the Twelfth Language Resources and Evaluation Conference. 2020

    2020

  20. [20]

    Quantifying the evolution of individual scientific impact

    Sinatra, Roberta and Wang, Dashun and Deville, Pierre and Song, Chaoming and Barab \'a si, Albert-L \'a szl \'o. Quantifying the evolution of individual scientific impact. Science. 2016. doi:10.1126/science.aaf5239

    work page doi:10.1126/science.aaf5239 2016

  21. [21]

    and B \"o rner, Katy and Evans, James A

    Fortunato, Santo and Bergstrom, Carl T. and B \"o rner, Katy and Evans, James A. and Helbing, Dirk and Milojevi \'c , Sta s a and Petersen, Alexander M. and Radicchi, Filippo and Sinatra, Roberta and Uzzi, Brian and Vespignani, Alessandro and Waltman, Ludo and Wang, Dashun and Barab \'a si, Albert-L \'a szl \'o. Science of science. Science. 2018. doi:10.1...

    work page doi:10.1126/science.aao0185 2018

  22. [22]

    What do citation counts measure? A review of studies on citing behavior

    Bornmann, Lutz and Daniel, Hans-Dieter. What do citation counts measure? A review of studies on citing behavior. Journal of Documentation. 2008. doi:10.1108/00220410810844150

    work page doi:10.1108/00220410810844150 2008

  23. [23]

    and Larivi \`e re, Vincent and Gingras, Yves

    Lozano, George A. and Larivi \`e re, Vincent and Gingras, Yves. The weakening relationship between the impact factor and papers' citations in the digital age. Journal of the American Society for Information Science and Technology. 2012. doi:10.1002/asi.22731

    work page doi:10.1002/asi.22731 2012

  24. [24]

    and Patterson, Marcia and Pulverer, Bernd and Swaminathan, Sowmya and Taylor, Stuart and Curry, Stephen and McNutt, Marcia

    Larivi \`e re, Vincent and Kiermer, V \'e ronique and MacCallum, Catriona J. and Patterson, Marcia and Pulverer, Bernd and Swaminathan, Sowmya and Taylor, Stuart and Curry, Stephen and McNutt, Marcia. A simple proposal for the publication of journal citation distributions. bioRxiv. 2016. doi:10.1101/062109

    work page doi:10.1101/062109 2016

  25. [25]

    and Koller, Alexander

    Bender, Emily M. and Koller, Alexander. Climbing towards NLU : On Meaning, Form, and Understanding in the Age of Data. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. doi:10.18653/v1/2020.acl-main.463

    work page doi:10.18653/v1/2020.acl-main.463 2020

  26. [26]

    and Gebru, Timnit and McMillan-Major, Angelina and Shmitchell, Shmargaret

    Bender, Emily M. and Gebru, Timnit and McMillan-Major, Angelina and Shmitchell, Shmargaret. On the Dangers of Stochastic Parrots: Can Language Models Be Too Big?. Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency (FAccT '21). 2021. doi:10.1145/3442188.3445922

    work page doi:10.1145/3442188.3445922 2021

  27. [27]

    COLM : The Conference on Language Modeling

    Conference on Language Modeling. COLM : The Conference on Language Modeling. 2024

    2024

  28. [28]

    Announcing the Transactions on Machine Learning Research

    Transactions on Machine Learning Research. Announcing the Transactions on Machine Learning Research. 2022

    2022

  29. [29]

    SPECTER : Document-level Representation Learning using Citation-informed Transformers

    Cohan, Arman and Feldman, Sergey and Beltagy, Iz and Downey, Doug and Weld, Daniel. SPECTER : Document-level Representation Learning using Citation-informed Transformers. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. doi:10.18653/v1/2020.acl-main.207

    work page doi:10.18653/v1/2020.acl-main.207 2020

  30. [30]

    S ci R ep E val: A Multi-Format Benchmark for Scientific Document Representations

    Singh, Amanpreet and D ' Arcy, Mike and Cohan, Arman and Downey, Doug and Feldman, Sergey. S ci R ep E val: A Multi-Format Benchmark for Scientific Document Representations. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. doi:10.18653/v1/2023.emnlp-main.338

    work page doi:10.18653/v1/2023.emnlp-main.338 2023

  31. [31]

    S 2 ORC : The Semantic Scholar Open Research Corpus

    Lo, Kyle and Wang, Lucy Lu and Neumann, Mark and Kinney, Rodney and Weld, Daniel. S 2 ORC : The Semantic Scholar Open Research Corpus. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. doi:10.18653/v1/2020.acl-main.447

    work page doi:10.18653/v1/2020.acl-main.447 2020

  32. [32]

    and Wagner, Linda and Wang, Lucy Lu and Wilhelm, Chris and Wu, Caroline and Yang, Jiangjiang and Zamarron, Angele and Van Zuylen, Madeleine and Weld, Daniel S

    Kinney, Rodney and Anastasiades, Chloe and Authur, Russell and Beltagy, Iz and Bragg, Jonathan and Buraczynski, Alexandra and Cachola, Isabel and Candra, Stefan and Chandrasekhar, Yoganand and Cohan, Arman and Crawford, Miles and Downey, Doug and Dunkelberger, Jason and Etzioni, Oren and Evans, Rob and Feldman, Sergey and Gorney, Joseph and Graham, David ...

    work page doi:10.48550/arxiv.2301.10140 2023

  33. [33]

    OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts

    Priem, Jason and Piwowar, Heather and Orr, Richard. O pen A lex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. arXiv preprint arXiv:2205.01833. 2022. doi:10.48550/arXiv.2205.01833

    work page internal anchor Pith review Pith/arXiv arXiv doi:10.48550/arxiv.2205.01833 2022

  34. [34]

    The Science of Science , publisher =

    Wang, Dashun and Barab. The Science of Science , publisher =. 2021 , isbn =

    2021

  35. [35]

    Eugene , title =

    Zeng, An and Shen, Zhesi and Zhou, Jianlin and Wu, Jinshan and Fan, Ying and Wang, Yougui and Stanley, H. Eugene , title =. Physics Reports , year =

  36. [36]

    and Uzzi, Brian , title =

    Wuchty, Stefan and Jones, Benjamin F. and Uzzi, Brian , title =. Science , year =

  37. [37]

    , title =

    Wu, Lingfei and Wang, Dashun and Evans, James A. , title =. Nature , year =

  38. [38]

    Principles of Scientific Research Team Formation and Evolution , journal =

    Milojevi. Principles of Scientific Research Team Formation and Evolution , journal =. 2014 , volume =

    2014

  39. [39]

    Newman, M. E. J. , title =. Proceedings of the National Academy of Sciences , year =

  40. [40]

    Science , year =

    Uzzi, Brian and Mukherjee, Satyam and Stringer, Michael and Jones, Ben , title =. Science , year =

  41. [41]

    and Rzhetsky, Andrey and Evans, James A

    Foster, Jacob G. and Rzhetsky, Andrey and Evans, James A. , title =. American Sociological Review , year =

  42. [42]

    , title =

    Park, Michael and Leahey, Erin and Funk, Russell J. , title =. Nature , year =

  43. [43]

    Quantifying Long-Term Scientific Impact , journal =

    Wang, Dashun and Song, Chaoming and Barab. Quantifying Long-Term Scientific Impact , journal =. 2013 , volume =

    2013

  44. [44]

    Proceedings of the National Academy of Sciences , year =

    Radicchi, Filippo and Fortunato, Santo and Castellano, Claudio , title =. Proceedings of the National Academy of Sciences , year =

  45. [45]

    and Weinberg, Bruce A

    Jones, Benjamin F. and Weinberg, Bruce A. , title =. Proceedings of the National Academy of Sciences , year =

  46. [46]

    Lee and Song, Chaoming and Wang, Dashun , title =

    Liu, Lu and Wang, Yang and Sinatra, Roberta and Giles, C. Lee and Song, Chaoming and Wang, Dashun , title =. Nature , year =

  47. [47]

    and Fortunato, Santo and Pan, Raj K

    Petersen, Alexander M. and Fortunato, Santo and Pan, Raj K. and Kaski, Kimmo and Penner, Orion and Rungi, Armando and Riccaboni, Massimo and Stanley, H. Eugene and Pammolli, Fabio , title =. Proceedings of the National Academy of Sciences , year =

  48. [48]

    and Sinatra, Roberta , title =

    Clauset, Aaron and Larremore, Daniel B. and Sinatra, Roberta , title =. Science , year =

  49. [49]

    , title =

    Clauset, Aaron and Arbesman, Samuel and Larremore, Daniel B. , title =. Science Advances , year =

  50. [50]

    Hunter and Zhang, Sam and Clauset, Aaron and Larremore, Daniel B

    Wapman, K. Hunter and Zhang, Sam and Clauset, Aaron and Larremore, Daniel B. , title =. Nature , year =

  51. [51]

    and Morgan, Allison C

    Way, Samuel F. and Morgan, Allison C. and Clauset, Aaron and Larremore, Daniel B. , title =. Proceedings of the National Academy of Sciences , year =

  52. [52]

    Eugene and Havlin, Shlomo , title =

    Zeng, An and Shen, Zhesi and Zhou, Jianlin and Fan, Ying and Di, Zengru and Wang, Yougui and Stanley, H. Eugene and Havlin, Shlomo , title =. Nature Communications , year =

  53. [53]

    , title =

    Jia, Tao and Wang, Dashun and Szymanski, Boleslaw K. , title =. Nature Human Behaviour , year =

  54. [54]

    , title =

    Merton, Robert K. , title =. Science , year =

  55. [55]

    , title =

    de Solla Price, Derek J. , title =. Science , year =

  56. [56]

    Management Science , year =

    Azoulay, Pierre and Stuart, Toby and Wang, Yanbo , title =. Management Science , year =

  57. [57]

    Examining Citations of Natural Language Processing Literature

    Mohammad, Saif M. Examining Citations of Natural Language Processing Literature. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. doi:10.18653/v1/2020.acl-main.464

    work page doi:10.18653/v1/2020.acl-main.464 2020

  58. [58]

    Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations

    Mohammad, Saif M. Gender Gap in Natural Language Processing Research: Disparities in Authorship and Citations. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. doi:10.18653/v1/2020.acl-main.702

    work page doi:10.18653/v1/2020.acl-main.702 2020

  59. [59]

    He Said, She Said: Gender in the ACL A nthology

    Vogel, Adam and Jurafsky, Dan. He Said, She Said: Gender in the ACL A nthology. Proceedings of the ACL -2012 Special Workshop on Rediscovering 50 Years of Discoveries. 2012

    2012

  60. [60]

    With Little Power Comes Great Responsibility

    Card, Dallas and Henderson, Peter and Khandelwal, Urvashi and Jia, Robin and Mahowald, Kyle and Jurafsky, Dan. With Little Power Comes Great Responsibility. Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP). 2020. doi:10.18653/v1/2020.emnlp-main.745

    work page doi:10.18653/v1/2020.emnlp-main.745 2020

  61. [61]

    and Yang, Diyi

    Rungta, Mukund and Singh, Janvijay and Mohammad, Saif M. and Yang, Diyi. Geographic Citation Gaps in NLP Research. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing. 2022. doi:10.18653/v1/2022.emnlp-main.89

    work page doi:10.18653/v1/2022.emnlp-main.89 2022

  62. [62]

    The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research

    Abdalla, Mohamed and Wahle, Jan Philip and Ruas, Terry and N \'e v \'e ol, Aur \'e lie and Ducel, Fanny and Mohammad, Saif and Fort, Karen. The Elephant in the Room: Analyzing the Presence of Big Tech in Natural Language Processing Research. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 20...

    work page doi:10.18653/v1/2023.acl-long.734 2023

  63. [63]

    We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields

    Wahle, Jan Philip and Ruas, Terry and Abdalla, Mohamed and Gipp, Bela and Mohammad, Saif. We are Who We Cite: Bridges of Influence Between Natural Language Processing and Other Academic Fields. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. doi:10.18653/v1/2023.emnlp-main.797

    work page doi:10.18653/v1/2023.emnlp-main.797 2023

  64. [64]

    What Do NLP Researchers Believe? Results of the NLP Community Metasurvey

    Michael, Julian and Holtzman, Ari and Parrish, Alicia and Mueller, Aaron and Wang, Alex and Chen, Angelica and Madaan, Divyam and Nangia, Nikita and Pang, Richard Yuanzhe and Phang, Jason and Bowman, Samuel R. What Do NLP Researchers Believe? Results of the NLP Community Metasurvey. Proceedings of the 61st Annual Meeting of the Association for Computation...

    work page doi:10.18653/v1/2023.acl-long.903 2023

  65. [65]

    On Forgetting to Cite Older Papers: An Analysis of the ACL A nthology

    Bollmann, Marcel and Elliott, Desmond. On Forgetting to Cite Older Papers: An Analysis of the ACL A nthology. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. doi:10.18653/v1/2020.acl-main.699

    work page doi:10.18653/v1/2020.acl-main.699 2020

  66. [66]

    A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?

    Pramanick, Aniket and Hou, Yufang and Mohammad, Saif and Gurevych, Iryna. A Diachronic Analysis of Paradigm Shifts in NLP Research: When, How, and Why?. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing. 2023. doi:10.18653/v1/2023.emnlp-main.142

    work page doi:10.18653/v1/2023.emnlp-main.142 2023

  67. [67]

    Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction

    Hou, Yufang and Jochim, Charles and Gleize, Martin and Bonin, Francesca and Ganguly, Debasis. Identification of Tasks, Datasets, Evaluation Metrics, and Numeric Scores for Scientific Leaderboards Construction. Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. 2019. doi:10.18653/v1/P19-1513

    work page doi:10.18653/v1/p19-1513 2019

  68. [68]

    Language (Technology) is Power: A Critical Survey of ``Bias'' in NLP

    Blodgett, Su Lin and Barocas, Solon and Daum \'e III, Hal and Wallach, Hanna. Language (Technology) is Power: A Critical Survey of ``Bias'' in NLP. Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. 2020. doi:10.18653/v1/2020.acl-main.485

    work page doi:10.18653/v1/2020.acl-main.485 2020

  69. [69]

    `Just What do You Think You ' re Doing, Dave?' A Checklist for Responsible Data Use in NLP

    Rogers, Anna and Baldwin, Timothy and Leins, Kobi. `Just What do You Think You ' re Doing, Dave?' A Checklist for Responsible Data Use in NLP. Findings of the Association for Computational Linguistics: EMNLP 2021. 2021. doi:10.18653/v1/2021.findings-emnlp.414

    work page doi:10.18653/v1/2021.findings-emnlp.414 2021

  70. [70]

    and Steinhardt, Jacob , title =

    Lipton, Zachary C. and Steinhardt, Jacob , title =. Queue , volume =. 2019 , doi =

    2019

  71. [71]

    and Snoek, Jasper and Wiltschko, Alex and Rahimi, Ali , title =

    Sculley, D. and Snoek, Jasper and Wiltschko, Alex and Rahimi, Ali , title =. 6th International Conference on Learning Representations,. 2018 , url =

    2018

  72. [72]

    State of the Art: Reproducibility in Artificial Intelligence , journal =

    Gundersen, Odd Erik and Kjensmo, Sigbj. State of the Art: Reproducibility in Artificial Intelligence , journal =. 2018 , doi =

    2018

  73. [73]

    Show Your Work: Improved Reporting of Experimental Results

    Dodge, Jesse and Gururangan, Suchin and Card, Dallas and Schwartz, Roy and Smith, Noah A. Show Your Work: Improved Reporting of Experimental Results. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019. doi:10.18653/v1/D19-1224

    work page doi:10.18653/v1/d19-1224 2019

  74. [74]

    Birhane, Abeba and Kalluri, Pratyusha and Card, Dallas and Agnew, William and Dotan, Ravit and Bao, Michelle , title =. 2022. 2022 , pages =

    2022

  75. [75]

    On the Opportunities and Risks of Foundation Models

    Bommasani, Rishi and Hudson, Drew A. and Adeli, Ehsan and Altman, Russ and Arora, Simran and von Arx, Sydney and others , title =. arXiv preprint arXiv:2108.07258 , year =

    work page internal anchor Pith review Pith/arXiv arXiv

  76. [76]

    and Wang, Dashun and Cebrian, Manuel and Rahwan, Iyad , title =

    Frank, Morgan R. and Wang, Dashun and Cebrian, Manuel and Rahwan, Iyad , title =. Nature Machine Intelligence , volume =. 2019 , doi =

    2019

  77. [77]

    Forecasting the Future of Artificial Intelligence with Machine Learning-Based Link Prediction in an Exponentially Growing Knowledge Network , journal =

    Krenn, Mario and Buffoni, Lorenzo and Coutinho, Bruno and Eppel, Sagi and Foster, Jacob Gates and Gritsevskiy, Andrew and Lee, Harlin and Lu, Yichao and Moutinho, Jo. Forecasting the Future of Artificial Intelligence with Machine Learning-Based Link Prediction in an Exponentially Growing Knowledge Network , journal =. 2023 , doi =

    2023

  78. [78]

    , title =

    Seglen, Per O. , title =. BMJ , volume =. 1997 , doi =

    1997

  79. [79]

    JAMA , volume =

    Garfield, Eugene , title =. JAMA , volume =. 2006 , doi =

    2006

  80. [80]

    Journal of Informetrics , volume =

    Waltman, Ludo , title =. Journal of Informetrics , volume =. 2016 , doi =

    2016

Showing first 80 references.