Sabharwal, Ashish

Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

Srivastava, A., Rastogi, A., Rao, A., Shoeb, A. A. M., Abid, A., Fisch, A., Brown, A. R., Santoro, A., Gupta, A., Garriga-Alonso, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 569

UNIFIEDQA: Crossing Format Boundaries with a Single QA System

Khashabi, D., Min, S., Khot, T., Sabharwal, A., Tafjord, O., Clark, P., & Hajishirzi, H. (2020) · Findings of the Association for Computational Linguistics: EMNLP 2020

Topic ModelingMultimodal Machine Learning Applications

Cited by 514

Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge

Clark, P., Cowhey, I., Etzioni, O., Khot, T., Sabharwal, A., Schoenick, C., & Tafjord, O. (2018) · arXiv

Topic ModelingMultimodal Machine Learning Applications

Cited by 462

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 349

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2023) · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingInformation Retrieval and Search Behavior

Cited by 349

♫ MuSiQue: Multihop Questions via Single-hop Question Composition

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2022) · Transactions of the Association for Computational Linguistics

Topic ModelingMultimodal Machine Learning Applications

Cited by 283

Is Multihop QA in DiRe Condition? Measuring and Reducing Disconnected Reasoning

Trivedi, H., Balasubramanian, N., Khot, T., & Sabharwal, A. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Topic ModelingMultimodal Machine Learning Applications

Cited by 29

Saturated Transformers are Constant-Depth Threshold Circuits

Merrill, W., Sabharwal, A., & Smith, N. A. (2022) · Transactions of the Association for Computational Linguistics

semigroups and automata theoryComplexity and Algorithms in Graphs

Cited by 25

The Parallelism Tradeoff: Limitations of Log-Precision Transformers

Merrill, W., & Sabharwal, A. (2023) · Transactions of the Association for Computational Linguistics

semigroups and automata theoryComplexity and Algorithms in Graphs

Cited by 22

QASC: A Dataset for Question Answering via Sentence Composition

Khot, T., Clark, P., Guerquin, M., Jansen, P., & Sabharwal, A. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingMultimodal Machine Learning Applications

Cited by 20

AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents

Trivedi, H., Khot, T., Hartmann, M., Manku, R., Dong, V., Li, E., Gupta, S., Sabharwal, A., & Balasubramanian, N. (2024) · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Software Engineering ResearchNatural Language Processing Techniques

Cited by 14

Improving Language Models via Plug-and-Play Retrieval Feedback

Yu, W., Zhang, Z., Liang, Z., Jiang, M., & Sabharwal, A. (2023) · arXiv

Topic ModelingInformation Retrieval and Search Behavior

Cited by 14

SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories

Bogin, B., Yang, K., Gupta, S., Richardson, K., Bransom, E., Clark, P., Sabharwal, A., & Khot, T. (2024) · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing

Natural Language Processing TechniquesTopic Modeling

Cited by 9

A Logic for Expressing Log-Precision Transformers

Merrill, W., & Sabharwal, A. (2023) · Advances in Neural Information Processing Systems 36

Logic, programming, and type systemsLogic, Reasoning, and Knowledge

Cited by 6

The Illusion of State in State-Space Models

Merrill, W., Petty, J., & Sabharwal, A. (2024) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 5

The expressive power of transformers with chain of thought

Merrill, W., & Sabharwal, A. (2023) · arXiv

semigroups and automata theoryComputability, Logic, AI Algorithms

Cited by 4

AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents

Trivedi, H., Khot, T., Hartmann, M., Manku, R., Dong, V., Li, E., Gupta, S., Sabharwal, A., & Balasubramanian, N. (2024) · arXiv

Software Engineering ResearchAI-based Problem Solving and Planning

Cited by 2

ZebraLogic: On the Scaling Limits of LLMs for Logical Reasoning

Lin, B. Y., Bras, R. L., Richardson, K., Sabharwal, A., Poovendran, R., Clark, P., & Choi, Y. (2025) · arXiv

Logic, Reasoning, and KnowledgeTopic Modeling

Cited by 1

SUPER: Evaluating Agents on Setting Up and Executing Tasks from Research Repositories

Bogin, B., Yang, K., Gupta, S., Richardson, K., Bransom, E., Clark, P., Sabharwal, A., & Khot, T. (2024) · arXiv

Software Engineering ResearchTopic Modeling

Cited by 0

Exact Expressive Power of Transformers with Padding

Merrill, W., & Sabharwal, A. (2025) · Advances in Neural Information Processing Systems 38

Computability, Logic, AI Algorithmssemigroups and automata theory

Cited by 0