🧑‍🔬 Ishita Dasgupta

🏛️ Affiliations

🏢Adobe SystemsSan Jose · United States2024–2026 · 7 paperscurrent🎓University of California, BerkeleyBerkeley · United States2026 · 1 papercurrent🎓Harvard UniversityCambridge · United States2017–2020 · 9 papers🏢GoogleMountain View · United States2021–2024 · 7 papers🎓Princeton UniversityPrinceton · United States2020–2023 · 7 papers🏢Google DeepMindLondon · United Kingdom2021–2024 · 6 papers🎓Massachusetts Institute of TechnologyCambridge · United States2023–2024 · 2 papers🎓Vellore Institute of Technology UniversityVellore · India2018–2021 · 2 papersEducation since 2018 · BTECH, Computer Science and Engineering🏢Tech MahindraPune · India2020Employment since 2020 · Project Trainee Intern

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 5 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

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

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. Can language models learn from explanations in context? (2022 · 295 citations)
   Large language models can pick up new tasks from a handful of in-context examples. The authors test whether adding explanations to those examples, as people do when teaching, also helps models. They took 40 difficult tasks and subtasks from BIG-bench. For each, one author wrote post-answer explanations for 15 sampled…
2. Language models show human-like content effects on reasoning tasks (2022 · 162 citations)
   Ideally, abstract reasoning should not depend on what a problem is about. Yet people reason better when a problem's meaning agrees with the logically correct answer. Psychologists call these "content effects." This paper asks whether large language models, whose expectations come from human-written text, blend…

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. Can language models learn from explanations in context?Findings of the Association for Computational LinguisticsAndrew Lampinen, Ishita Dasgupta, Stephanie Chan, et al.2022 · 295 citations
2. Language models show human-like content effects on reasoning tasksarXiv:2207.07051Ishita Dasgupta, Andrew K. Lampinen, Stephanie C. Y. Chan, et al.2022 · 162 citations
3. Collaborating with language models for embodied reasoning ↗arXiv:2302.00763 · no summary yetIshita Dasgupta et al.2023 · 36 citations
4. PIVOT: Iterative Visual Prompting Elicits Actionable Knowledge for VLMs ↗arXiv (Cornell University) · no summary yetSoroush Nasiriany et al.2024 · 21 citations
5. Distilling Internet-Scale Vision-Language Models into Embodied Agents ↗arXiv:2301.12507 · no summary yetTheodore Sumers et al.2023 · 9 citations
6. Is This Just Fantasy? Language Model Representations Reflect Human Judgments of Event Plausibility ↗arXiv (Cornell University) · no summary yetMichael A. Lepori et al.2025

🤝 Co-authors here

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

• Andrew Lampinen ×2
• Antonia Creswell ×2
• Felix Hill ×2
• James L. McClelland ×2
• Stephanie Chan ×2

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 2022

🔗 Cited by

Papers of our library cite their works 3,833 times (of the 149,439 papers whose bibliographies we read); 884 citing papers with a page here →.

Updated 2 November 2026

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