Clark, Peter

Memory-assisted prompt editing to improve GPT-3 after deployment

Madaan, A., Tandon, N., Clark, P., & Yang, Y. (2022) · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Topic ModelingNatural Language Processing Techniques

Cited by 60

Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning

Lu, P., Qiu, L., Chang, K.-W., Wu, Y. N., Zhu, S.-C., Rajpurohit, T., Clark, P., & Kalyan, A. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 45

DISCOVERYWORLD: A Virtual Environment for Developing and Evaluating Automated Scientific Discovery Agents

Jansen, P., Côté, M.-A., Khot, T., Bransom, E., Mishra, B. D., Majumder, B. P., Tafjord, O., & Clark, P. (2024) · arXiv

Multi-Agent Systems and NegotiationScientific Computing and Data Management

Cited by 9

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

Introduction

Clark, P. (2013) · Oxford Handbooks Online

Global Urban Networks and DynamicsUrban Development and Societal Issues

Cited by 0