---
title: "ACL (A*): papers and universities"
description: "ACL (Association for Computational Linguistics) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 5,662 of its papers from 2024–2026 (1,036 in 2024, 1,971 in 2025, 2,655 in 2026). By the countries of the authors' institutions, the most papers come from China (2,419), the United States (1,523) and Hong Kong (394)."
url: https://scholar4ai.com/proceedings/acl
markdown_url: https://scholar4ai.com/proceedings/acl.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "ACL (Association for Computational Linguistics) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 5,662 of its papers from 2024–2026 (1,036 in 2024, 1,971 in 2025, 2,655 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)
---

# ACL: Association for Computational Linguistics — papers and institutions, 2024–2026

Papers of ACL, 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.

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

## Key findings

- ACL (Association for Computational Linguistics) is rated A* by the CORE Conference Rankings (ICORE2026); OpenAlex lists 5,662 of its papers from 2024–2026 (1,036 in 2024, 1,971 in 2025, 2,655 in 2026).
- By the countries of the authors' institutions, the most papers come from China (2,419), the United States (1,523) and Hong Kong (394).
- The institutions with the most papers are Tsinghua University (294), Peking University (245) and Zhejiang University (238).
- Its most-cited paper of these years is “ChatDev: Communicative Agents for Software Development” (2024), with 450 citations.
- OpenAlex has affiliations for only 87% of its papers, so the institution and country counts are incomplete.

## Papers per year

*ACL papers per publication year in OpenAlex (–: not in OpenAlex yet)*

| Year | Papers | With affiliations |
|---|---:|---:|
| 2024 | 1,036 | 929 |
| 2025 | 1,971 | 1,715 |
| 2026 | 2,655 | 2,277 |

## Sources

*Where OpenAlex lists the papers*

| OpenAlex source | Which papers |
|---|---|
| [S4393915740](https://openalex.org/S4393915740) | The conference's proceedings |

## Who publishes there

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

| # | Institution | Papers |
|---:|---|---:|
| 1 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 294 |
| 2 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 245 |
| 3 | [Zhejiang University](https://scholar4ai.com/institutions/zhejiang-university.md) | 238 |
| 4 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 225 |
| 5 | [University of Chinese Academy of Sciences](https://scholar4ai.com/institutions/university-of-chinese-academy-of-sciences.md) | 199 |
| 6 | [Harbin Institute of Technology](https://scholar4ai.com/institutions/harbin-institute-of-technology.md) | 177 |
| 7 | Alibaba Group (China) | 156 |
| 8 | [National University of Singapore](https://scholar4ai.com/institutions/national-university-of-singapore.md) | 155 |
| 9 | [Shanghai Jiao Tong University](https://scholar4ai.com/institutions/shanghai-jiao-tong-university.md) | 155 |
| 10 | [Fudan University](https://scholar4ai.com/institutions/fudan-university.md) | 149 |

*Countries with the most ACL papers, 2024–2026*

| # | Country | Papers |
|---:|---|---:|
| 1 | China | 2,419 |
| 2 | United States | 1,523 |
| 3 | Hong Kong | 394 |
| 4 | United Kingdom | 335 |
| 5 | Singapore | 311 |
| 6 | Germany | 270 |
| 7 | South Korea | 248 |
| 8 | Canada | 173 |
| 9 | Australia | 171 |
| 10 | Japan | 161 |

*Keywords with the most ACL papers, 2024–2026*

| # | Keyword | Papers |
|---:|---|---:|
| 1 | [large language models](https://scholar4ai.com/keywords/large-language-models.md) | 2,415 |
| 2 | retrieval-augmented generation | 235 |
| 3 | benchmark | 207 |
| 4 | [vision-language models](https://scholar4ai.com/keywords/vision-language-models.md) | 163 |
| 5 | low-resource languages | 152 |
| 6 | multimodal large language models | 147 |
| 7 | LLM agents | 125 |
| 8 | benchmarking | 120 |
| 9 | [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 120 |
| 10 | [reinforcement learning](https://scholar4ai.com/keywords/reinforcement-learning.md) | 115 |

## Most-cited papers

*Most-cited ACL papers of 2024–2026 (citations counted by OpenAlex)*

| # | Citations | Title | Published | Authors |
|---:|---:|---|---|---|
| 1 | 450 | [ChatDev: Communicative Agents for Software Development](https://doi.org/10.18653/v1/2024.acl-long.810) | 2024-01-01 | Chen Qian, Wei Liu, Hongzhang Liu et al. |
| 2 | 444 | [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://doi.org/10.18653/v1/2024.acl-long.679) | 2024-01-01 | Muhammad Maaz, Hanoona Rasheed, Salman Hameed Khan et al. |
| 3 | 434 | [LlamaFactory: Unified Efficient Fine-Tuning of 100+ Language Models](https://doi.org/10.18653/v1/2024.acl-demos.38) | 2024-01-01 | Yaowei Zheng, Richong Zhang, Junhao Zhang et al. |
| 4 | 294 | [DeepSeekMoE: Towards Ultimate Expert Specialization in Mixture-of-Experts Language Models](https://doi.org/10.18653/v1/2024.acl-long.70) | 2024-01-01 | Damai Dai, Chengqi Deng, Chenggang Zhao et al. |
| 5 | 216 | [Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference](https://doi.org/10.18653/v1/2025.acl-long.127) | 2025-01-01 | Benjamin C. Warner, Antoine Chaffin, Benjamin Clavié et al. |
| 6 | 186 | [LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding](https://doi.org/10.18653/v1/2024.acl-long.172) | 2024-01-01 | Yushi Bai, Xin Lv, Jiajie Zhang et al. |
| 7 | 150 | [Improving Text Embeddings with Large Language Models](https://doi.org/10.18653/v1/2024.acl-long.642) | 2024-01-01 | Liang Wang, Nan Yang, Xiaolong Huang et al. |
| 8 | 138 | [Large Language Models are not Fair Evaluators](https://doi.org/10.18653/v1/2024.acl-long.511) | 2024-01-01 | Peiyi Wang, Lei Li, Liang Chen et al. |
| 9 | 119 | [Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models](https://doi.org/10.18653/v1/2024.acl-long.818) | 2024-01-01 | Mosh Levy, Alon Jacoby, Yoav Goldberg |
| 10 | 119 | [RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models](https://doi.org/10.18653/v1/2024.acl-long.585) | 2024-01-01 | Cheng Niu, Yuanhao Wu, Juno Zhu et al. |

## Cite this page

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

```bibtex
@misc{scholar4ai-proceedings-acl,
  author = {{Scholar4AI}},
  title = {ACL: Association for Computational Linguistics — papers and institutions, 2024–2026},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/proceedings/acl}},
  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/).