Arkil Patel

According to our database, Arkil Patel authored at least 15 papers between 2019 and 2026.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2026

DeepSeek-R1 Thoughtology: Let's think about LLM reasoning.

Sara Vera Marjanovic, Arkil Patel, Vaibhav Adlakha, Milad Aghajohari, Parishad BehnamGhader, Mehar Bhatia, Aditi Khandelwal, Austin Kraft, Benno Krojer, Xing Han Lù, Nicholas Meade, Dongchan Shin, Amirhossein Kazemnejad, Gaurav Kamath, Marius Mosbach, Karolina Stanczak, Siva Reddy

Trans. Mach. Learn. Res., 2026

2025

AgentRewardBench: Evaluating Automatic Evaluations of Web Agent Trajectories.

Xing Han Lù, Amirhossein Kazemnejad, Nicholas Meade, Arkil Patel, Dongchan Shin, Alejandra Zambrano, Karolina Stanczak, Peter Shaw, Christopher J. Pal, Siva Reddy

CoRR, April, 2025

DeepSeek-R1 Thoughtology: Let's about LLM Reasoning.

Sara Vera Marjanovic, Arkil Patel, Vaibhav Adlakha, Milad Aghajohari, Parishad BehnamGhader, Mehar Bhatia, Aditi Khandelwal, Austin Kraft, Benno Krojer, Xing Han Lù, Nicholas Meade, Dongchan Shin, Amirhossein Kazemnejad, Gaurav Kamath, Marius Mosbach, Karolina Stanczak, Siva Reddy

CoRR, April, 2025

How to Get Your LLM to Generate Challenging Problems for Evaluation.

Arkil Patel, Siva Reddy, Dzmitry Bahdanau

CoRR, February, 2025

SafeArena: Evaluating the Safety of Autonomous Web Agents.

Ada Defne Tur, Nicholas Meade, Xing Han Lù, Alejandra Zambrano, Arkil Patel, Esin Durmus, Spandana Gella, Karolina Stanczak, Siva Reddy

Proceedings of the Forty-second International Conference on Machine Learning, 2025

2024

Universal Adversarial Triggers Are Not Universal.

Nicholas Meade, Arkil Patel, Siva Reddy

CoRR, 2024

Evaluating In-Context Learning of Libraries for Code Generation.

Arkil Patel, Siva Reddy, Dzmitry Bahdanau, Pradeep Dasigi

Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), 2024

Understanding In-Context Learning in Transformers and LLMs by Learning to Learn Discrete Functions.

Satwik Bhattamishra, Arkil Patel, Phil Blunsom, Varun Kanade

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

MAGNIFICo: Evaluating the In-Context Learning Ability of Large Language Models to Generalize to Novel Interpretations.

Arkil Patel, Satwik Bhattamishra, Siva Reddy, Dzmitry Bahdanau

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions.

Satwik Bhattamishra, Arkil Patel, Varun Kanade, Phil Blunsom

Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2023

2022

When Can Transformers Ground and Compose: Insights from Compositional Generalization Benchmarks.

Ankur Sikarwar, Arkil Patel, Navin Goyal

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022

Revisiting the Compositional Generalization Abilities of Neural Sequence Models.

Arkil Patel, Satwik Bhattamishra, Phil Blunsom, Navin Goyal

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), 2022

2021

Are NLP Models really able to Solve Simple Math Word Problems?

Arkil Patel, Satwik Bhattamishra, Navin Goyal

Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, 2021

2020

On the Computational Power of Transformers and Its Implications in Sequence Modeling.

Satwik Bhattamishra, Arkil Patel, Navin Goyal

Proceedings of the 24th Conference on Computational Natural Language Learning, 2020

2019

VehicleChain: Blockchain-based Vehicular Data Transmission Scheme for Smart City.

Arkil Patel, Naigam Shah, Trupil Limbasiya, Debasis Das

Proceedings of the 2019 IEEE International Conference on Systems, Man and Cybernetics, 2019