EACL: European Association for Computational Linguistics — papers and institutions, 2024–2026

Papers of EACL, European 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#

EACL (European Association for Computational Linguistics) is rated A by the CORE Conference Rankings (ICORE2026); OpenAlex lists 891 of its papers from 2024–2026 (250 in 2024, 641 in 2026; 2025 not in OpenAlex yet).
By the countries of the authors' institutions, the most papers come from the United States (257), Germany (112) and the United Kingdom (85).
The institutions with the most papers are Amazon (United States) (19), Technische Universität Darmstadt (18) and Carnegie Mellon University (18).
Its most-cited paper of these years is “RAGAs: Automated Evaluation of Retrieval Augmented Generation” (2024), with 665 citations.
OpenAlex has affiliations for only 82% of its papers, so the institution and country counts are incomplete.

Papers per year#

EACL papers per publication year in OpenAlex (–: not in OpenAlex yet)
Year	Papers	With affiliations
2024	250	231
2025	–	–
2026	641	503

Sources#

Where OpenAlex lists the papers
OpenAlex source	Which papers
S4306418011	The conference's proceedings

Who publishes there#

Institutions with the most EACL papers, 2024–2026
#	Institution	Papers
1	Amazon (United States)	19
2	Technische Universität Darmstadt	18
3	Carnegie Mellon University	18
4	University of Edinburgh	18
5	Centre National de la Recherche Scientifique	16
6	Mohamed bin Zayed University of Artificial Intelligence	16
7	Johns Hopkins University	15
8	University of Maryland, College Park	15
9	The University of Tokyo	15
10	University of Cambridge	14

Countries with the most EACL papers, 2024–2026
#	Country	Papers
1	United States	257
2	Germany	112
3	United Kingdom	85
4	Japan	50
5	China	49
6	Canada	42
7	India	35
8	South Korea	35
9	Switzerland	33
10	Italy	33

Keywords with the most EACL papers, 2024–2026
#	Keyword	Papers
1	large language models	291
2	low-resource languages	43
3	benchmark	29
4	retrieval-augmented generation	29
5	vision-language models	28
6	language models	25
7	chatbots	19
8	fine-tuning	17
9	machine translation	17
10	named entity recognition	17

Most-cited papers#

Most-cited EACL papers of 2024–2026 (citations counted by OpenAlex)
#	Citations	Title	Published	Authors
1	665	RAGAs: Automated Evaluation of Retrieval Augmented Generation	2024-01-01	Shahul Es, Jithin James, Luis Espinosa-Anke et al.
2	70	Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMs	2024-01-01	Simone Balloccu, Patrícia Schmidtová, Mateusz Lango et al.
3	44	Clever Hans or Neural Theory of Mind? Stress Testing Social Reasoning in Large Language Models	2024-01-01	Natalie Shapira, Mosh Levy, Seyed Hossein Alavi et al.
4	37	M4: Multi-generator, Multi-domain, and Multi-lingual Black-Box Machine-Generated Text Detection	2024-01-01	Yuxia Wang, Jonibek Mansurov, Petar Ivanov et al.
5	29	Learning to Retrieve In-Context Examples for Large Language Models	2024-01-01	Liang Wang, Nan Yang, Furu Wei
6	28	LAraBench: Benchmarking Arabic AI with Large Language Models	2024-01-01	Ahmed Abdelalí, Hamdy Mubarak, Shammur Absar Chowdhury et al.
7	27	MLCopilot: Unleashing the Power of Large Language Models in Solving Machine Learning Tasks	2024-01-01	Lei Zhang, Yuge Zhang, Kan Ren et al.
8	25	Generating Benchmarks for Factuality Evaluation of Language Models	2024-01-01	Dor Muhlgay, Ori Ram, Inbal Magar et al.
9	25	LLM Comparative Assessment: Zero-shot NLG Evaluation through Pairwise Comparisons using Large Language Models	2024-01-01	Adian Liusie, Potsawee Manakul, Mark Gales
10	24	MEGAnno+: A Human-LLM Collaborative Annotation System	2024-01-01	Hannah Kim, Kushan Mitra, Rafael Li Chen et al.

Conferences in the same field#

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

Cite this page

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

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

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