Nie, Allen

On the Opportunities and Risks of Foundation Models

Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., et al. (2021) · arXiv

Ethics and Social Impacts of AIArtificial Intelligence Applications

Cited by 2,676

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

The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances

Nie, A., Chandak, Y., Suzara, M., Malik, A., Woodrow, J., Peng, M., Sahami, M., Brunskill, E., & Piech, C. (2024) · arXiv

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 19

The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances

Nie, A., Chandak, Y., Suzara, M., Malik, A., Woodrow, J., Peng, M., Sahami, M., Brunskill, E., & Piech, C. (2024)

Teaching and Learning ProgrammingArtificial Intelligence in Education

Cited by 19

Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs

Cheng, C.-A., Nie, A., & Swaminathan, A. (2024) · arXiv

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 4

Trace is the Next AutoDiff: Generative Optimization with Rich Feedback, Execution Traces, and LLMs

Cheng, C.-A., Nie, A., & Swaminathan, A. (2024) · Advances in Neural Information Processing Systems 37

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 4

LLF-Bench: Benchmark for Interactive Learning from Language Feedback

Cheng, C.-A., Kolobov, A., Misra, D., Nie, A., & Swaminathan, A. (2023) · arXiv

Reinforcement Learning in RoboticsAI-based Problem Solving and Planning

Cited by 1

Understanding the Challenges in Iterative Generative Optimization with LLMs

Nie, A., Daull, X., Kuang, Z., Akkiraju, A., Chaudhuri, A., Piasevoli, M., Rong, R., Yuan, Y., Choudhary, P., Xiao, S., et al. (2026) · arXiv

Evolutionary Algorithms and ApplicationsSoftware Engineering Research

Cited by 0