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Gagan Bansal

Gagan Bansal has 2 papers in our library, in 2023–2025, most of them on Language models & pretraining, Evaluation & benchmarks; often with Ahmed Hassan Awadallah, Beibin Li, Eric Horvitz.

2 papers in our library · 2023–2025

OpenAlex · ORCID · Wikidata · h-index 17 · 31 works · 2,768 citations OpenAlex, 2026-10-14

📄 2 works in our library · 🔗 Cited 490 times in our library · 2 more in our library without a page yet · ⭐ Paper Stars ranking: #3382

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

🎓University of WashingtonSeattle · United States2018–2022 · 12 papersEducation🏢MicrosoftRedmond · United States2023–2025 · 8 papers🏢GoogleMountain View · United States2018–2019 · 3 papers🎓Indian Institute of Technology DelhiNew Delhi · IndiaEducation
🏛️Microsoft ResearchUnited StatesEmployment

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

 Fields, topics and concepts

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

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 2 worksArts and Humanities · 2 worksComputer Science · 2 worksLanguage and Linguistics · 2 works

🧭 Topics 2Research subjects and languages the work is about.Evaluation & benchmarks · 1 workLanguage models & pretraining · 1 work

 Works by year

2020s

20251 paper20231 paper

 Papers

The most cited first (OpenAlex and our library).

  1. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent Conversation (2023 · 606 citations)
    Building LLM applications that coordinate several capabilities (reasoning, tools, people) usually means writing custom glue code for each workflow.
  2. Navigating Rifts in Human-LLM Grounding: Study and Benchmark (2025 · 19 citations)
    Conversational LLMs are tuned mainly to execute instructions. They rarely do the joint work people use to build shared understanding, such as checking assumptions, asking what was meant, or proposing next steps. The authors studied this gap using logs from three sources: WildChat, Bing Chat, and the Wizard-of-Oz…

All works

4 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. AutoGen: Enabling Next-Gen LLM Applications via Multi-Agent ConversationarXiv:2308.08155Qingyun Wu, Gagan Bansal, Jieyu Zhang, et al.2023 · 606 citations
  2. Updates in Human-AI Teams: Understanding and Addressing the Performance/Compatibility Tradeoff ↗Proceedings of the AAAI Conference on Artificial Intelligence · no summary yetGagan Bansal et al.2019 · 292 citations
  3. Gmail Smart Compose: Real-Time Assisted Writing ↗arXiv (Cornell University) · no summary yetMia Xu Chen et al.2019 · 89 citations
  4. Navigating Rifts in Human-LLM Grounding: Study and BenchmarkAnnual Meeting of the Association for Computational Linguistics (ACL)Omar Shaikh, Hussein Mozannar, Gagan Bansal, et al.2025 · 19 citations

 Co-authors here

10 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 490 times (of the 149,439 papers whose bibliographies we read); 84 citing papers with a page here →.

Updated

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