---
title: "EMNLP (A*): papers and universities"
description: "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)."
url: https://scholar4ai.com/proceedings/emnlp
markdown_url: https://scholar4ai.com/proceedings/emnlp.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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). (as of 2026-11-02)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-02T12:20:52+09:00
window: 2026-10-04 to 2026-11-02
source: OpenAlex (CC0)
---

# 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.

All conferences: https://scholar4ai.com/proceedings.md

## 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](https://openalex.org/S4363608991) | The conference's proceedings |

## Who publishes there

*Institutions with the most EMNLP papers, 2024–2026*

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 265 |
| 2 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 228 |
| 3 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 196 |
| 4 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 186 |
| 5 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 169 |
| 6 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 156 |
| 7 | [Fudan University](https://scholar4ai.com/institutions/fudan-university.md) | 152 |
| 8 | Alibaba Group (China) | 149 |
| 9 | [Nanyang Technological University](https://scholar4ai.com/institutions/nanyang-technological-university.md) | 146 |
| 10 | [University of Science and Technology of China](https://scholar4ai.com/institutions/university-of-science-and-technology-of-china.md) | 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](https://scholar4ai.com/keywords/large-language-models.md) | 2,509 |
| 2 | retrieval-augmented generation | 278 |
| 3 | [vision-language models](https://scholar4ai.com/keywords/vision-language-models.md) | 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](https://scholar4ai.com/keywords/reinforcement-learning.md) | 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](https://doi.org/10.18653/v1/2024.emnlp-main.64) | 2024-01-01 | Qingxiu Dong, Lei Li, Damai Dai et al. |
| 2 | 442 | [Video-LLaVA: Learning United Visual Representation by Alignment Before Projection](https://doi.org/10.18653/v1/2024.emnlp-main.342) | 2024-01-01 | Bin Lin, Yang Ye, Bin Zhu et al. |
| 3 | 320 | [Encouraging Divergent Thinking in Large Language Models through Multi-Agent Debate](https://doi.org/10.18653/v1/2024.emnlp-main.992) | 2024-01-01 | Tian Liang, Zhiwei He, Wenxiang Jiao et al. |
| 4 | 179 | [Large Language Models for Data Annotation and Synthesis: A Survey](https://doi.org/10.18653/v1/2024.emnlp-main.54) | 2024-01-01 | Zhen Tan, Dawei Li, Song Wang et al. |
| 5 | 143 | [Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://doi.org/10.18653/v1/2024.emnlp-main.15) | 2024-01-01 | Oded Ovadia, Menachem Brief, Moshik Mishaeli et al. |
| 6 | 132 | [Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction](https://doi.org/10.18653/v1/2024.emnlp-main.548) | 2024-01-01 | Bowen Zhang, Harold Soh |
| 7 | 125 | [Searching for Best Practices in Retrieval-Augmented Generation](https://doi.org/10.18653/v1/2024.emnlp-main.981) | 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](https://doi.org/10.18653/v1/2025.emnlp-main.138) | 2025-01-01 | Dawei Li, Bohan Jiang, Liangjie Huang et al. |
| 9 | 118 | [ORPO: Monolithic Preference Optimization without Reference Model](https://doi.org/10.18653/v1/2024.emnlp-main.626) | 2024-01-01 | Jiwoo Hong, Noah Lee, James Thorne |
| 10 | 104 | [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://doi.org/10.18653/v1/2024.emnlp-industry.103) | 2024-01-01 | Xin Zhang, Yanzhao Zhang, Dingkun Long et al. |

## 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

```bibtex
@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}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).