🧑‍🔬 Howard Chen

🏛️ Affiliations

🎓Princeton UniversityPrinceton · United States2021–2026 · 11 paperscurrent🎓University of Illinois ChicagoChicago · United States2026 · 1 papercurrent🎓University of IowaIowa City · United States2012–2017 · 18 papers🎓Auburn UniversityAuburn · United States2016–2023 · 14 papers🎓University of Alabama in HuntsvilleHuntsville · United States2023–2025 · 9 papers🏢IBMArmonk · United States2004–2010 · 9 papers🏢United MicroelectronicsHsinchu · Taiwan2009–2016 · 8 papers🔬IBM Research - Thomas J. Watson Research CenterYorktown Heights · United States2004–2012 · 8 papers🏢Merck & Co., Inc., Rahway, NJ, USARahway · United States1991–2010 · 7 papers🎓University of MinnesotaMinneapolis · United States2003–2005 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 15 more institutions on single bylines are not listed.

🧭 Concepts, shelves and topics

Of its works in our library: the number of works on each and its rating among them.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents (2022 · 387 citations)
   WebShop is a benchmark for agents that must read natural-language goals and act on web pages. Earlier interactive benchmarks either used toy language or needed heavy human effort to collect data and judge outcomes. WebShop simulates an online store with 1.18 million products scraped from amazon.com and 12,087…
2. What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task Learning (2023 · 100 citations)
   The paper asks what language models actually do when they solve tasks from a few in-context examples. Do they simply identify a task they already know from pre-training, or do they acquire new input-label associations from the prompt? The authors separate these into two abilities. Task recognition (TR) means…

All works

6 works of theirs in our library, the most cited first; 2 with a page here, the others link their original until we summarise them.

1. WebShop: Towards Scalable Real-World Web Interaction with Grounded Language AgentsShunyu Yao, Howard Chen, John Yang, et al.Conference on Neural Information Processing Systems (NeurIPS) · 387 citations
2. CharXiv: Charting Gaps in Realistic Chart Understanding in Multimodal LLMs · original ↗ · no summary yet · arXiv:2406.18521Zirui Wang et al.2024 · 116 citations
3. What In-Context Learning “Learns” In-Context: Disentangling Task Recognition and Task LearningJane Pan, Tianyu Gao, Howard Chen, et al.Findings of the Association for Computational Linguistics · 100 citations
4. Language Models as Science Tutors · original ↗ · no summary yet · arXiv:2402.11111Alexis Chevalier et al.2024 · 29 citations
5. COLLIE: Systematic Construction of Constrained Text Generation Tasks · original ↗ · no summary yet · arXiv:2307.08689Shunyu Yao et al.2023 · 23 citations
6. Statutory Construction and Interpretation for Artificial Intelligence · original ↗ · no summary yet · arXiv:2509.01186Luxi He et al.2025 · 2 citations

🤝 Co-authors here

5 co-authors with a page, the most papers together first.

• Danqi Chen
• John Yang
• Karthik Narasimhan
• Shunyu Yao
• Tianyu Gao

Venues

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

• Findings of the Association for Computational Linguistics: Findings of the Association for Computational Linguistics 2023
• Conference on Neural Information Processing Systems (NeurIPS): NeurIPS 2022

🔗 Cited by

Papers of our library cite their works 494 times (of the 141,840 papers whose bibliographies we read); 50 citing papers with a page here →.

Updated 24 October 2026

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