🧑‍🔬 Arkil Patel

🏛️ Affiliations

🎓McGill UniversityMontreal · Canada2022–2026 · 11 paperscurrent🔬Mila - Quebec Artificial Intelligence InstituteMontreal · Canada2022–2026 · 5 paperscurrent🎓University of OxfordOxford · United Kingdom2022–2023 · 5 papers🏢Microsoft ResearchBengaluru · India2020–2022 · 3 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 3 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. Are NLP Models really able to Solve Simple Math Word Problems? (2021 · 890 citations)
   The paper tests whether neural math word problem solvers really understand simple one-unknown arithmetic problems.
2. DeepSeek-R1 Thoughtology: Let's think about LLM Reasoning (2025 · 47 citations)
   Large Reasoning Models (LRMs) produce long reasoning chains before giving an answer. Their internal behaviour has been hard to study because OpenAI's o1 hides its thoughts. DeepSeek-R1 exposes its reasoning, so the authors use it to launch a research programme they call "Thoughtology."

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. Are NLP Models really able to Solve Simple Math Word Problems?Arkil Patel, Satwik Bhattamishra, Navin GoyalMova's core list · 890 citations
2. DeepSeek-R1 Thoughtology: Let's think about LLM ReasoningarXiv:2504.07128Sara Vera Marjanovic, Arkil Patel, Vaibhav Adlakha, et al.2025 · 47 citations
3. Evaluating In-Context Learning of Libraries for Code Generation ↗Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers) · no summary yetArkil Patel et al.2024 · 9 citations
4. How to Get Your LLM to Generate Challenging Problems for Evaluation ↗arXiv (Cornell University) · no summary yetArkil Patel, Siva Reddy, Dzmitry Bahdanau2025 · 6 citations
5. MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations ↗Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing · no summary yetArkil Patel et al.2023 · 5 citations
6. Forecasting Downstream Performance of LLMs With Proxy Metrics ↗arXiv (Cornell University) · no summary yetArkil Patel et al.2026

🤝 Co-authors here

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

• Benno Krojer
• Dongchan Shin
• Mehar Bhatia
• Milad Aghajohari
• Navin Goyal
• Nicholas Meade
• Parishad BehnamGhader
• Sara Vera Marjanovic
• Satwik Bhattamishra
• Vaibhav Adlakha
• Xing Han Lu

Venues

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

• Conference of the North American Chapter of the Association for Computational Linguistics (NAACL): NAACL 2021
• Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
• Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)

🔗 Cited by

Papers of our library cite their works 660 times (of the 149,439 papers whose bibliographies we read); 226 citing papers with a page here →.

Updated 3 November 2026

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