EMNLP: Empirical Methods in Natural Language Processing — papers and institutions, 2024–2026

Papers of EMNLP, Empirical Methods in Natural Language Processing (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#

EMNLP (Empirical Methods in Natural Language Processing) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 6,321 of its papers from 2024–2026 (1,391 in 2024, 2,003 in 2025, 2,927 in 2026).
By the countries of the authors' institutions, the most papers come from China (2,242), the United States (1,914) and South Korea (434).
The institutions with the most papers are Tsinghua University (265), Zhejiang University (228) and Chinese Academy of Sciences (196).
Its most-cited paper of these years is “A Survey on In-context Learning” (2024), with 650 citations.
OpenAlex has affiliations for only 90% of its papers, so the institution and country counts are incomplete.

Papers per year#

EMNLP papers per publication year in OpenAlex (–: not in OpenAlex yet)
Year	Papers	With affiliations
2024	1,391	1,247
2025	2,003	1,772
2026	2,927	2,649

Sources#

Where OpenAlex lists the papers
OpenAlex source	Which papers
S4363608991	The conference's proceedings

Who publishes there#

Institutions with the most EMNLP papers, 2024–2026
#	Institution	Papers
1	Tsinghua University	265
2	Zhejiang University	228
3	Chinese Academy of Sciences	196
4	Peking University	186
5	University of Chinese Academy of Sciences	169
6	Shanghai Jiao Tong University	156
7	Fudan University	152
8	Alibaba Group (China)	149
9	Nanyang Technological University	146
10	University of Science and Technology of China	126

Countries with the most EMNLP papers, 2024–2026
#	Country	Papers
1	China	2,242
2	United States	1,914
3	South Korea	434
4	United Kingdom	422
5	Hong Kong	382
6	Germany	373
7	Singapore	300
8	Canada	234
9	Japan	188
10	Australia	183

Keywords with the most EMNLP papers, 2024–2026
#	Keyword	Papers
1	large language models	2,509
2	retrieval-augmented generation	278
3	vision-language models	250
4	benchmark	235
5	LLM agents	207
6	multimodal large language models	167
7	low-resource languages	143
8	question answering	132
9	fine-tuning	130
10	reinforcement learning	130

Most-cited papers#

Most-cited EMNLP papers of 2024–2026 (citations counted by OpenAlex)
#	Citations	Title	Published	Authors
1	650	A Survey on In-context Learning	2024-01-01	Qingxiu Dong, Lei Li, Damai Dai et al.
2	442	Video-LLaVA: Learning United Visual Representation by Alignment Before Projection	2024-01-01	Bin Lin, Yang Ye, Bin Zhu et al.
3	320	Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate	2024-01-01	Tian Liang, Zhiwei He, Wenxiang Jiao et al.
4	179	Large Language Models for Data Annotation and Synthesis: A Survey	2024-01-01	Zhen Tan, Dawei Li, Song Wang et al.
5	143	Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs	2024-01-01	Oded Ovadia, Menachem Brief, Moshik Mishaeli et al.
6	132	Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction	2024-01-01	Bowen Zhang, Harold Soh
7	125	Searching for Best Practices in Retrieval-Augmented Generation	2024-01-01	Xiaohua Wang, Zhenhua Wang, Xuan Gao et al.
8	118	From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judge	2025-01-01	Dawei Li, Bohan Jiang, Liangjie Huang et al.
9	118	ORPO: Monolithic Preference Optimization without Reference Model	2024-01-01	Jiwoo Hong, Noah Lee, James Thorne
10	104	mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval	2024-01-01	Xin Zhang, Yanzhao Zhang, Dingkun Long et al.

Conferences in the same field#

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

Cite this page

Scholar4AI. (2026). EMNLP: Empirical Methods in Natural Language Processing — papers and institutions, 2024–2026. Journal and Conference Research Rankings. Retrieved November 2, 2026, from https://scholar4ai.com/proceedings/emnlp

@misc{scholar4ai-proceedings-emnlp,
  author = {{Scholar4AI}},
  title = {EMNLP: Empirical Methods in Natural Language Processing — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/emnlp}},
  urldate = {2026-11-02},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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