🧑‍🔬 Chandan Singh

🏛️ Affiliations

🏢MicrosoftRedmond · United States2022–2026 · 12 paperscurrent🎓Punjabi UniversityPatiāla · India2002–2025 · 35 papers🎓University of California, BerkeleyBerkeley · United States2018–2023 · 12 papers🎓Dr. Sarvepalli Radhakrishnan Rajasthan Ayurved UniversityJodhpur · India2021–2023 · 6 papers🎓Babasaheb Bhimrao Ambedkar UniversityLucknow · India2025 · 3 papers🎓Cornell UniversityIthaca · United States2023 · 3 papers🎓Harvard UniversityCambridge · United States2023 · 2 papers🎓SRM Institute of Science and TechnologyChennai · India2022–2023 · 2 papers🎓University of VirginiaCharlottesville · United States2017–2018 · 2 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 10 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. Rethinking Interpretability in the Era of Large Language Models (2024 · 87 citations)
   This position paper from Microsoft Research asks how interpretable machine learning should evolve now that large language models can describe patterns in plain language.
2. Self-Verification Improves Few-Shot Clinical Information Extraction (2023 · 51 citations)
   Clinical information extraction often lacks labeled training data. Few-shot prompting of LLMs can stand in for supervised training, but its outputs are still error-prone and hard to inspect, which matters in a safety-critical field like medicine. The authors propose self-verification (SV), a four-call chain in which…

All works

7 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. Rethinking Interpretability in the Era of Large Language ModelsarXiv:2402.01761Chandan Singh, Jeevana Priya Inala, Michel Galley, et al.2024 · 87 citations
2. Self-Verification Improves Few-Shot Clinical Information ExtractionarXiv:2306.00024 · IMLH 2023Zelalem Gero, Chandan Singh, Hao Cheng, et al.2023 · 51 citations
3. OMNIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities ↗arXiv (Cornell University) · no summary yetVerma, Sahil et al.2025 · 4 citations
4. Attribute Structuring Improves LLM-Based Evaluation of Clinical Text Summaries ↗arXiv (Cornell University) · no summary yetZelalem Gero et al.2024 · 4 citations
5. MULTIGUARD: An Efficient Approach for AI Safety Moderation Across Languages and Modalities ↗Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing · no summary yetSahil Verma et al.2025 · 3 citations
6. Model Tells Itself Where to Attend: Faithfulness Meets Automatic Attention Steering ↗arXiv (Cornell University) · no summary yetQingru Zhang et al.2024 · 3 citations
7. Reinforcement Learning over Patient Trajectories for Clinical Reasoning in EHR Foundation Models ↗arXiv (Cornell University) · no summary yetYuxin Xiao et al.2026

🤝 Co-authors here

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

• Jianfeng Gao ×2
• Michel Galley ×2
• Hao Cheng
• Hoifung Poon
• Tristan Naumann

Venues

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

• Northern European Journal of Language Technology
• Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing

🔗 Cited by

Papers of our library cite their works 1,368 times (of the 149,439 papers whose bibliographies we read); 322 citing papers with a page here →.

Updated 30 October 2026

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