---
title: "NAACL (A): papers and universities"
description: "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)."
url: https://scholar4ai.com/proceedings/naacl
markdown_url: https://scholar4ai.com/proceedings/naacl.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "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… (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)
---

# 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.

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

## 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](https://openalex.org/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](https://scholar4ai.com/institutions/stanford-university.md) | 38 |
| 4 | [Peking University](https://scholar4ai.com/institutions/peking-university.md) | 35 |
| 5 | [Tsinghua University](https://scholar4ai.com/institutions/tsinghua-university.md) | 34 |
| 6 | University of Southern California | 32 |
| 7 | Amazon (United States) | 31 |
| 8 | Allen Institute for Artificial Intelligence | 31 |
| 9 | [Seoul National University](https://scholar4ai.com/institutions/seoul-national-university.md) | 29 |
| 10 | [Chinese Academy of Sciences](https://scholar4ai.com/institutions/chinese-academy-of-sciences.md) | 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](https://scholar4ai.com/keywords/large-language-models.md) | 591 |
| 2 | language models | 60 |
| 3 | low-resource languages | 56 |
| 4 | retrieval-augmented generation | 46 |
| 5 | chatbots | 41 |
| 6 | [knowledge distillation](https://scholar4ai.com/keywords/knowledge-distillation.md) | 39 |
| 7 | [vision-language models](https://scholar4ai.com/keywords/vision-language-models.md) | 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](https://doi.org/10.18653/v1/2024.naacl-long.389) | 2024-01-01 | Soyeong Jeong, Jinheon Baek, Sukmin Cho et al. |
| 2 | 209 | [REPLUG: Retrieval-Augmented Black-Box Language Models](https://doi.org/10.18653/v1/2024.naacl-long.463) | 2024-01-01 | Weijia Shi, Sewon Min, Michihiro Yasunaga et al. |
| 3 | 182 | [Better Zero-Shot Reasoning with Role-Play Prompting](https://doi.org/10.18653/v1/2024.naacl-long.228) | 2024-01-01 | Aobo Kong, Shiwan Zhao, Hao Chen et al. |
| 4 | 177 | [GPTScore: Evaluate as You Desire](https://doi.org/10.18653/v1/2024.naacl-long.365) | 2024-01-01 | Jinlan Fu, See-Kiong Ng, Zhengbao Jiang et al. |
| 5 | 149 | [ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://doi.org/10.18653/v1/2024.naacl-long.20) | 2024-01-01 | Jon Saad-Falcon, Omar Khattab, Christopher Potts et al. |
| 6 | 114 | [Can Knowledge Graphs Reduce Hallucinations in LLMs? : A Survey](https://doi.org/10.18653/v1/2024.naacl-long.219) | 2024-01-01 | Garima Agrawal, Tharindu Kumarage, Zeyad Alghamdi et al. |
| 7 | 108 | [TopicGPT: A Prompt-based Topic Modeling Framework](https://doi.org/10.18653/v1/2024.naacl-long.164) | 2024-01-01 | Chau Pham, Alexander Hoyle, Simeng Sun et al. |
| 8 | 97 | [GLiNER: Generalist Model for Named Entity Recognition using Bidirectional Transformer](https://doi.org/10.18653/v1/2024.naacl-long.300) | 2024-01-01 | Urchade Zaratiana, Nadi Tomeh, Pierre Holat et al. |
| 9 | 97 | [Reducing hallucination in structured outputs via Retrieval-Augmented Generation](https://doi.org/10.18653/v1/2024.naacl-industry.19) | 2024-01-01 | Orlando Ayala, Patrice Bechard |
| 10 | 97 | [AnnoLLM: Making Large Language Models to Be Better Crowdsourced Annotators](https://doi.org/10.18653/v1/2024.naacl-industry.15) | 2024-01-01 | Xingwei He, Zhenghao Lin, Yeyun Gong et al. |

## 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

```bibtex
@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}
}
```

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