Narasimhan, Karthik

Tree of Thoughts: Deliberate Problem Solving with Large Language Models

Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., & Narasimhan, K. (2023) · arXiv

AI-based Problem Solving and PlanningTopic Modeling

Cited by 1,158

Reflexion: Language Agents with Verbal Reinforcement Learning

Shinn, N., Cassano, F., Berman, E., Gopinath, A., Narasimhan, K., & Yao, S. (2023) · arXiv

Reinforcement Learning in RoboticsSpeech and dialogue systems

Cited by 1,087

ReAct: Synergizing Reasoning and Acting in Language Models

Yao, S., Zhao, J., Yu, D., Du, N., Shafran, I., Narasimhan, K., & Cao, Y. (2022) · arXiv

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 608

SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering

Yang, J., Jimenez, C., Wettig, A., Lieret, K., Yao, S., Narasimhan, K., & Press, O. (2024) · Advances in Neural Information Processing Systems 37

Advanced Software Engineering MethodologiesSoftware Engineering Techniques and Practices

Cited by 264

Toxicity in ChatGPT: Analyzing Persona-assigned Language Models

Deshpande, A., Murahari, V., Rajpurohit, T., Kalyan, A., & Narasimhan, K. (2023) · arXiv

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 178

Toxicity in chatgpt: Analyzing persona-assigned language models

Deshpande, A., Murahari, V., Rajpurohit, T., Kalyan, A., & Narasimhan, K. (2023) · Findings of the Association for Computational Linguistics: EMNLP 2023

Ethics and Social Impacts of AIHate Speech and Cyberbullying Detection

Cited by 178

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Yao, S., Chen, H., Yang, J., & Narasimhan, K. (2022) · arXiv

Speech and dialogue systemsMulti-Agent Systems and Negotiation

Cited by 107

WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents

Yao, S., Chen, H., Yang, J., & Narasimhan, K. (2022) · Advances in Neural Information Processing Systems 35

Speech and dialogue systemsMulti-Agent Systems and Negotiation

Cited by 107

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs

Chaudhari, S., Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A., & da Silva, B. C. (2024) · arXiv

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 92

RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs

Chaudhari, S., Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A., & Castro da Silva, B. (2026) · ACM Computing Surveys

Reinforcement Learning in RoboticsNatural Language Processing Techniques

Cited by 92

Keep CALM and Explore: Language Models for Action Generation in Text-based Games

Yao, S., Rao, R., Hausknecht, M., & Narasimhan, K. (2020) · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 75

SWE-bench: Can Language Models Resolve Real-World GitHub Issues?

Jimenez, C. E., Yang, J., Wettig, A., Yao, S., Pei, K., Press, O., & Narasimhan, K. (2023) · arXiv

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 72

Cognitive Architectures for Language Agents

Sumers, T. R., Yao, S., Narasimhan, K., & Griffiths, T. L. (2023) · arXiv

AI-based Problem Solving and PlanningPsychiatry, Mental Health, Neuroscience

Cited by 72

GEO: Generative Engine Optimization

Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., & Deshpande, A. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining

Information Retrieval and Search BehaviorWeb visibility and informetrics

Cited by 62

Anthropomorphization of AI: Opportunities and Risks

Deshpande, A., Rajpurohit, T., Narasimhan, K., & Kalyan, A. (2023) · Proceedings of the Natural Legal Language Processing Workshop 2023

Ethics and Social Impacts of AISocial Robot Interaction and HRI

Cited by 57

Can Rationalization Improve Robustness?

Chen, H., He, J., Narasimhan, K., & Chen, D. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Natural Language Processing TechniquesTopic Modeling

Cited by 31

InterCode: Standardizing and Benchmarking Interactive Coding with Execution Feedback

Yang, J., Prabhakar, A., Narasimhan, K., & Yao, S. (2023) · arXiv

Teaching and Learning ProgrammingSoftware Engineering Research

Cited by 26

Calibration, Entropy Rates, and Memory in Language Models

Braverman, M., Chen, X., Kakade, S. M., Narasimhan, K., Zhang, C., & Zhang, Y. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 17

Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning

Hanjie, A. W., Zhong, V., & Narasimhan, K. (2021) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 15

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Kumar, S., Correa, C. G., Dasgupta, I., Marjieh, R., Hu, M. Y., Hawkins, R., Cohen, J. D., Daw, N., Narasimhan, K., & Griffiths, T. (2022) · Advances in Neural Information Processing Systems 35

Natural Language Processing TechniquesMachine Learning and Algorithms

Cited by 10

$τ$-bench: A Benchmark for Tool-Agent-User Interaction in Real-World Domains

Yao, S., Shinn, N., Razavi, P., & Narasimhan, K. (2024) · arXiv

Information Retrieval and Search BehaviorAI-based Problem Solving and Planning

Cited by 9

FireAct: Toward Language Agent Fine-tuning

Chen, B., Shu, C., Shareghi, E., Collier, N., Narasimhan, K., & Yao, S. (2023) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 9

Using Natural Language and Program Abstractions to Instill Human Inductive Biases in Machines

Kumar, S., Correa, C. G., Dasgupta, I., Marjieh, R., Hu, M. Y., Hawkins, R. D., Daw, N. D., Cohen, J. D., Narasimhan, K., & Griffiths, T. L. (2022) · arXiv

Natural Language Processing TechniquesMachine Learning and Algorithms

Cited by 9

SemSup: Semantic Supervision for Simple and Scalable Zero-shot Generalization

Hanjie, A. W., Deshpande, A., & Narasimhan, K. (2022) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 4