---
title: "Research Triangle — research institutions and their papers"
description: "Research Triangle (United States): 3 institutions with papers on Elamur Research — 3 universities — and 39,545 citations of their works."
canonical: "https://research.elamur.ai/free/en/agglomerations/research-triangle/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/agglomerations/research-triangle/)

[home](https://md.research.elamur.ai/free/en/) / [institutions](https://md.research.elamur.ai/free/en/orgs/) / [agglomerations](https://md.research.elamur.ai/free/en/agglomerations/) / Research Triangle

# Research Triangle

Agglomeration [United States](https://md.research.elamur.ai/free/en/countries/united-states/) · metro population 1,611,391 (the Raleigh functional urban area, GHSL 2015)

**3** institutions with a page**120** works in our library**39,545** citations of their works

## Institutions (3)

3 universities.

### 🎓 Universities (3)

1. **[University of North Carolina at Chapel Hill](https://md.research.elamur.ai/free/en/universities/university-of-north-carolina-at-chapel-hill/)**University · Chapel Hill, [United States](https://md.research.elamur.ai/free/en/countries/united-states/)*74 works · 23,278 citations*
2. **[Duke University](https://md.research.elamur.ai/free/en/universities/duke-university/)**University · Durham, [United States](https://md.research.elamur.ai/free/en/countries/united-states/)*37 works · 15,861 citations*
3. **[North Carolina State University](https://md.research.elamur.ai/free/en/universities/north-carolina-state-university/)**University · Raleigh, [United States](https://md.research.elamur.ai/free/en/countries/united-states/)*14 works · 406 citations*

## Works in our library (120)

Works of its institutions, the most cited first.

1. **[Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/) [Nature Machine Intelligence](https://md.research.elamur.ai/free/en/venues/nature-machine-intelligence/)**Cynthia Rudin*2018 · 10,460 citations*
2. **[Statsmodels: econometric and statistical modeling with Python](https://md.research.elamur.ai/free/en/papers/seabold-2010-statsmodels-econometric-statistical-modeling-python/) [Proceedings of the Python in Science Conferences](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-python-in-science-conferences/)**Skipper Seabold, Josef Perktold*2010 · 6,540 citations*
3. **[LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://md.research.elamur.ai/free/en/papers/tan-2019-lxmert-learning-cross-modality-encoder/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hao Tan, Mohit Bansal*2019 · 2,789 citations*
4. [**Emergent Abilities of Large Language Models arXiv:2206.07682**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,269 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
5. **[ReferItGame: Referring to Objects in Photographs of Natural Scenes](https://md.research.elamur.ai/free/en/papers/kazemzadeh-2014-referitgame-referring-objects-photographs-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, et al.*2014 · 1,317 citations*
6. **[Adversarial NLI: A New Benchmark for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/nie-2020-adversarial-nli-new-benchmark-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Nie, Adina Williams, Emily Dinan, et al.*2020 · 1,296 citations*
7. [**What Makes Good In-Context Examples for GPT-3? Proceedings of Deep Learning Inside Out (DeeLIO 2022): The 3rd Workshop on Knowledge Extraction and Integration for Deep Learning Architectures**Jiachang Liu, Dinghan Shen, Yizhe Zhang, et al.*2022 · 1,256 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-what-makes-good-context-examples/)
8. **[Modeling Context in Referring Expressions](https://md.research.elamur.ai/free/en/papers/yu-2016-modeling-context-referring-expressions/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Licheng Yu, Patrick Poirson, Shan Yang, et al.*2016 · 1,203 citations*
9. **[Fast Abstractive Summarization with Reinforce-Selected Sentence Rewriting](https://md.research.elamur.ai/free/en/papers/chen-2018-fast-abstractive-summarization-reinforce-selected/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yen-Chun Chen, Mohit Bansal*2018 · 826 citations*
10. **[A Novel Cascade Binary Tagging Framework for Relational Triple Extraction](https://md.research.elamur.ai/free/en/papers/wei-2020-novel-cascade-binary-tagging-framework/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhepei Wei, Jianlin Su, Yue Wang, et al.*2020 · 736 citations*
11. **[Entity-Based Cross-Document Coreferencing Using the Vector Space Model](https://md.research.elamur.ai/free/en/papers/bagga-1998-entity-based-cross-document-coreferencing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Amit Bagga, Breck Baldwin*1998 · 701 citations*
12. **[TVQA: Localized, Compositional Video Question Answering](https://md.research.elamur.ai/free/en/papers/lei-2018-tvqa-localized-compositional-video-question/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jie Lei, Licheng Yu, Mohit Bansal, et al.*2018 · 687 citations*
13. **[Joint Embedding of Words and Labels for Text Classification](https://md.research.elamur.ai/free/en/papers/wang-2018-joint-embedding-words-labels-text/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guoyin Wang, Chunyuan Li, Wenlin Wang, et al.*2018 · 465 citations*
14. **[Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms](https://md.research.elamur.ai/free/en/papers/shen-2018-baseline-needs-more-love-simple/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Dinghan Shen, Guoyin Wang, Wenlin Wang, et al.*2018 · 386 citations*
15. **[Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://md.research.elamur.ai/free/en/papers/tan-2019-learning-navigate-unseen-environments-back/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hao Tan, Licheng Yu, Mohit Bansal*2019 · 358 citations*
16. **[Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?](https://md.research.elamur.ai/free/en/papers/hase-2020-evaluating-explainable-ai-which-algorithmic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peter Hase, Mohit Bansal*2020 · 318 citations*
17. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/)
18. **[TreeTalk: Composition and Compression of Trees for Image Descriptions](https://md.research.elamur.ai/free/en/papers/kuznetsova-2014-treetalk-composition-compression-trees-image/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Polina Kuznetsova, Vicente Ordonez, Tamara L. Berg, et al.*2014 · 290 citations*
19. **[Contextualized Perturbation for Textual Adversarial Attack](https://md.research.elamur.ai/free/en/papers/li-2021-contextualized-perturbation-textual-adversarial-attack/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Dianqi Li, Yizhe Zhang, Hao Peng, et al.*2021 · 283 citations*
20. [**Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning OSDI 2022**Lianmin Zheng, Zhuohan Li, Hao Zhang, et al.*2022 · 279 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/)
21. **[Humanity's Last Exam](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 256 citations*
22. **[Multi-Reward Reinforced Summarization with Saliency and Entailment](https://md.research.elamur.ai/free/en/papers/pasunuru-2018-multi-reward-reinforced-summarization-saliency/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Ramakanth Pasunuru, Mohit Bansal*2018 · 255 citations*
23. **[An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jiaao Chen, Derek Tam, Colin Raffel, et al.*2023 · 254 citations*
24. **[ASQA: Factoid Questions Meet Long-Form Answers](https://md.research.elamur.ai/free/en/papers/stelmakh-2022-asqa-factoid-questions-meet-long/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, et al.*2022 · 245 citations*
25. [**TrustLLM: Trustworthiness in Large Language Models arXiv:2401.05561**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 237 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)

[See all 120 works in Research Triangle →](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/works/)

## Works by year

### 2020s

[**2026** 32 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2026/) · [**2025** 9 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2025/) · [**2024** 13 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2024/) · [**2023** 19 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2023/) · [**2022** 7 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2022/) · [**2021** 5 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2021/) · [**2020** 10 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2020/)

### 2010s

[**2019** 6 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2019/) · [**2018** 9 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2018/) · [**2016** 1 paper](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2016/) · [**2014** 3 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2014/) · [**2013** 1 paper](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2013/) · [**2011** 1 paper](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2011/) · [**2010** 2 papers](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2010/)

### 2000s

[**2007** 1 paper](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/2007/)

### 1990s

[**1998** 1 paper](https://md.research.elamur.ai/free/en/agglomerations/research-triangle/year/1998/)

## Fields, topics and concepts

Of its works in our library, with the number of works on each.

📚 Shelves 38 The field of science this work belongs to (OpenAlex fields).[Computer Science · 117 works](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Artificial Intelligence · 109 works](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · [Arts and Humanities · 97 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Language and Linguistics · 97 works](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · [Computer Vision and Pattern Recognition · 35 works](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · [Social Sciences · 11 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/) · [Mathematics · 3 works](https://md.research.elamur.ai/free/en/shelves/mathematics/) · [Safety Research · 6 works](https://md.research.elamur.ai/free/en/shelves/social-sciences/safety-research/) · [Statistics and Probability · 1 work](https://md.research.elamur.ai/free/en/shelves/mathematics/statistics-and-probability/) · [Engineering · 3 works](https://md.research.elamur.ai/free/en/shelves/engineering/) · [Safety, Risk, Reliability and Quality · 1 work](https://md.research.elamur.ai/free/en/shelves/engineering/safety-risk-reliability-and-quality/) · [Information Systems · 15 works](https://md.research.elamur.ai/free/en/shelves/computer-science/information-systems/) · [Psychology · 5 works](https://md.research.elamur.ai/free/en/shelves/psychology/) · [Experimental and Cognitive Psychology · 4 works](https://md.research.elamur.ai/free/en/shelves/psychology/experimental-and-cognitive-psychology/) · [Software · 3 works](https://md.research.elamur.ai/free/en/shelves/computer-science/software/)+23 more

🧭 Topics 27 Research subjects and languages the work is about.[Language models & pretraining · 27 works](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning · 23 works](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Summarization · 4 works](https://md.research.elamur.ai/free/en/topics/summarization/) · [Question answering · 4 works](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Evaluation & benchmarks · 8 works](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Information extraction · 1 work](https://md.research.elamur.ai/free/en/topics/information-extraction/) · [Information retrieval & search · 3 works](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · [Vision & language · 9 works](https://md.research.elamur.ai/free/en/topics/multimodal/) · [Efficient models & compression · 2 works](https://md.research.elamur.ai/free/en/topics/efficient-models/) · [Code & programming languages · 1 work](https://md.research.elamur.ai/free/en/topics/code-and-programming/) · [Dialogue systems · 7 works](https://md.research.elamur.ai/free/en/topics/dialogue-systems/) · [Speech act theory · 6 works](https://md.research.elamur.ai/free/en/topics/speech-act-theory/) · [Interpretability · 2 works](https://md.research.elamur.ai/free/en/topics/interpretability/) · [Coreference resolution · 2 works](https://md.research.elamur.ai/free/en/topics/coreference-resolution/) · [English · 5 works](https://md.research.elamur.ai/free/en/topics/english/)+12 more

💡 Concepts 13 Key ideas and methods explained on our concept pages.[Machine learning · 1 work](https://md.research.elamur.ai/free/en/concepts/machine-learning/) · [Transformer and attention · 1 work](https://md.research.elamur.ai/free/en/concepts/transformer/) · [Natural language understanding · 1 work](https://md.research.elamur.ai/free/en/concepts/natural-language-understanding/) · [Text summarization · 1 work](https://md.research.elamur.ai/free/en/concepts/text-summarization/) · [Information extraction · 1 work](https://md.research.elamur.ai/free/en/concepts/information-extraction/) · [Question answering · 1 work](https://md.research.elamur.ai/free/en/concepts/question-answering/) · [Language model · 1 work](https://md.research.elamur.ai/free/en/concepts/language-model/) · [Large language models · 1 work](https://md.research.elamur.ai/free/en/concepts/large-language-models/) · [Text classification · 1 work](https://md.research.elamur.ai/free/en/concepts/text-classification/) · [GloVe · 1 work](https://md.research.elamur.ai/free/en/concepts/glove/) · [Distributional semantics · 1 work](https://md.research.elamur.ai/free/en/concepts/distributional-semantics/) · [TF-IDF · 1 work](https://md.research.elamur.ai/free/en/concepts/tf-idf/) · [Semantics · 1 work](https://md.research.elamur.ai/free/en/concepts/semantics/)

Agglomerations: the GHSL Urban Centre Database 2025; populations: the GHSL functional urban areas 2015 (urban centre and commuting zone), else the urban centre, as each page says (European Commission, Joint Research Centre, CC BY 4.0). An institution belongs to the urban centre its coordinates (ROR, OpenAlex) fall in, or one within 5 km. Land: Natural Earth.

- [← back to](https://md.research.elamur.ai/free/en/countries/united-states/) — United States

Updated 2 November 2026