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Hunter Lang

Hunter Lang has 3 papers in our library, in 2022–2026, most of them on Language models & pretraining; often with David Sontag, Monica Agrawal, Stefan Hegselmann.

3 papers in our library · 2022–2026

OpenAlex · h-index 7 · 34 works · 532 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 219 times in our library · 5 more in our library without a page yet · ⭐ Paper Stars ranking: #2751

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓Massachusetts Institute of TechnologyCambridge · United States2017–2026 · 11 paperscurrent🔬MIT Computer Science and Artificial Intelligence LaboratoryCambridge · United States2021–2022 · 5 papers🏢MicrosoftRedmond · United States2019–2021 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 1 more institution on single bylines are not listed.

 Fields, topics and concepts

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

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 worksBiochemistry, Genetics and Molecular Biology · 2 worksMolecular Biology · 2 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 1 work

💡 Concepts 1Key ideas and methods explained on our concept pages.Few-shot learning · 1 work

 Works by year

2020s

20261 paper20222 papers

 Papers

The most cited first (OpenAlex and our library).

  1. Large language models are few-shot clinical information extractors (2022 · 454 citations)
    Extracting structured variables from clinical notes is difficult. The text is full of jargon and irregular phrasing, annotation requires medical experts, and most annotated corpora have data use agreements that forbid sending them to external LLM APIs. The authors ask whether a general-domain model, InstructGPT…
  2. Large Language Models are Few-Shot Clinical Information Extractors (2022 · 384 citations)
    Clinical notes contain many variables that never reach the structured fields of electronic health records.
  3. When One LLM Drools, Multi-LLM Collaboration Rules (2026 · 14 citations)
    This position paper by Feng et al. (ACL 2026) argues that a single general-purpose LLM cannot reliably handle complex, context-dependent, or subjective settings. It contributes no new dataset or model. Instead, it makes a conceptual case that any one model leaves gaps on three fronts. It misses parts of real-world…

All works

8 works of theirs in our library, the most cited first; 3 with a page here, the others link their original until we summarise them.

  1. Large language models are few-shot clinical information extractorsConference on Empirical Methods in Natural Language Processing (EMNLP)Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.2022 · 454 citations
  2. Large Language Models are Few-Shot Clinical Information ExtractorsConference on Empirical Methods in Natural Language Processing (EMNLP)Monica Agrawal, Stefan Hegselmann, Hunter Lang, et al.2022 · 384 citations
  3. Co-training Improves Prompt-based Learning for Large Language Models ↗arXiv (Cornell University) · no summary yetHunter Lang et al.2022 · 41 citations
  4. Learning to Decode Collaboratively with Multiple Language Models ↗Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · no summary yetZejiang Shen et al.2024 · 35 citations
  5. When One LLM Drools, Multi-LLM Collaboration RulesAnnual Meeting of the Association for Computational Linguistics (ACL)Shangbin Feng, Wenxuan Ding, Alisa Liu, et al.2026 · 14 citations
  6. Self-Supervised Self-Supervision by Combining Deep Learning and Probabilistic Logic ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetHunter Lang, Hoifung Poon2021 · 14 citations
  7. Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning ↗arXiv (Cornell University) · no summary yetHoifung Poon, Hai Wang, Hunter Lang2021 · 1 citation
  8. Chapter 14. Combining Probabilistic Logic and Deep Learning for Self-Supervised Learning ↗Frontiers in artificial intelligence and applications · no summary yetHoifung Poon, Hai Wang, Hunter Lang2021

 Co-authors here

12 of 15 co-authors with a page, the most papers together first.

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

 Cited by

Papers of our library cite their works 219 times (of the 149,439 papers whose bibliographies we read); 42 citing papers with a page here →.

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