NAACL: North American Association for Computational Linguistics — papers and institutions, 2024–2026

Papers of NAACL, North American Association for Computational Linguistics (CORE rank A; field: Artificial intelligence), 2024–2026: per year, the institutions, countries and topics, and the most-cited papers. Ranks are those of the CORE Conference Rankings, edition ICORE2026 (portal.core.edu.au), applied to every year shown. Papers are the works OpenAlex lists for the conference's proceedings, or for the issues of a conference-linked journal that publishes its papers (such as PACMPL, PVLDB or ACM TOG), by publication year; workshops, companion volumes, posters and Findings are left out. A paper counts once for each institution and country among its authors' affiliations as OpenAlex assigns them; papers without affiliations in OpenAlex count for none. For conferences published in book series that OpenAlex files as one source (LNCS, LIPIcs, SIAM and IOS Press proceedings), the papers are found by DOI: those of the volumes with the conference's title in Crossref, or LIPIcs DOIs, which name the conference. Conferences without DOIs (PMLR, USENIX, OpenReview) are listed without counts. A dash (–) for a year means OpenAlex has no papers of the conference for it yet: the proceedings are not published or indexed yet, or the conference did not meet that year (some meet every other year). 2026 counts what OpenAlex had indexed by 2026-11-02. Also as Markdown.

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Key findings#

NAACL (North American Association for Computational Linguistics) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 1,571 of its papers from 2024–2026 (662 in 2024, 909 in 2025; 2026 not in OpenAlex yet).
By the countries of the authors' institutions, the most papers come from the United States (693), China (304) and the United Kingdom (125).
The institutions with the most papers are Carnegie Mellon University (54), University of Washington (41) and Stanford University (38).
Its most-cited paper of these years is “Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity” (2024), with 258 citations.
OpenAlex has affiliations for only 89% of its papers, so the institution and country counts are incomplete.

Papers per year#

NAACL papers per publication year in OpenAlex (–: not in OpenAlex yet)
Year	Papers	With affiliations
2024	662	593
2025	909	806
2026	–	–

Sources#

Where OpenAlex lists the papers
OpenAlex source	Which papers
S4363608774	The conference's proceedings

Who publishes there#

Institutions with the most NAACL papers, 2024–2026
#	Institution	Papers
1	Carnegie Mellon University	54
2	University of Washington	41
3	Stanford University	38
4	Peking University	35
5	Tsinghua University	34
6	University of Southern California	32
7	Amazon (United States)	31
8	Allen Institute for Artificial Intelligence	31
9	Seoul National University	29
10	Chinese Academy of Sciences	29

Countries with the most NAACL papers, 2024–2026
#	Country	Papers
1	United States	693
2	China	304
3	United Kingdom	125
4	South Korea	110
5	Germany	102
6	Japan	58
7	Hong Kong	56
8	Canada	55
9	India	52
10	Singapore	51

Keywords with the most NAACL papers, 2024–2026
#	Keyword	Papers
1	large language models	591
2	language models	60
3	low-resource languages	56
4	retrieval-augmented generation	46
5	chatbots	41
6	knowledge distillation	39
7	vision-language models	36
8	benchmarking	35
9	benchmark	34
10	in-context learning	32

Most-cited papers#

Most-cited NAACL papers of 2024–2026 (citations counted by OpenAlex)
#	Citations	Title	Published	Authors
1	258	Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity	2024-01-01	Soyeong Jeong, Jinheon Baek, Sukmin Cho et al.
2	209	REPLUG: Retrieval-Augmented Black-Box Language Models	2024-01-01	Weijia Shi, Sewon Min, Michihiro Yasunaga et al.
3	182	Better Zero-Shot Reasoning with Role-Play Prompting	2024-01-01	Aobo Kong, Shiwan Zhao, Hao Chen et al.
4	177	GPTScore: Evaluate as You Desire	2024-01-01	Jinlan Fu, See-Kiong Ng, Zhengbao Jiang et al.
5	149	ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems	2024-01-01	Jon Saad-Falcon, Omar Khattab, Christopher Potts et al.
6	114	Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey	2024-01-01	Garima Agrawal, Tharindu Kumarage, Zeyad Alghamdi et al.
7	108	TopicGPT: A Prompt-based Topic Modeling Framework	2024-01-01	Chau Pham, Alexander Hoyle, Simeng Sun et al.
8	97	GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer	2024-01-01	Urchade Zaratiana, Nadi Tomeh, Pierre Holat et al.
9	97	Reducing hallucination in structured outputs via Retrieval-Augmented Generation	2024-01-01	Orlando Ayala, Patrice Bechard
10	97	AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators	2024-01-01	Xingwei He, Zhenghao Lin, Yeyun Gong et al.

Conferences in the same field#

AAAI · EMNLP · ACL · ICRA · IJCAI · HRI · EC · KR · ICAPS · IROS · Interspeech · AAMAS · CIKM · ECAI · EACL · GECCO · IJCAR · CP · SAT · CADE · FOGA

Cite this page

Scholar4AI. (2026). NAACL: North American Association for Computational Linguistics — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/naacl

@misc{scholar4ai-proceedings-naacl,
  author = {{Scholar4AI}},
  title = {NAACL: North American Association for Computational Linguistics — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/naacl}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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