Feedback-driven Generation
Feedback-Driven Generation is a generative methodology within interactive systems that iteratively refines output through continuous integration of user or system-provided feedback as a primary mechanism for adaptive content creation.
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2017–2026
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Thomas Reps, Tim Teitelbaum · ACM SIGPLAN Notices · 1984 · 371 citations
Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT
Wei Dai, Jionghao Lin, Hua Jin et al. · 2023 · 242 citations
Education, Educational Assessment, Educational Technology +17
Self-Refine: Iterative Refinement with Self-Feedback
Aman Madaan, Niket Tandon, Prakhar Gupta et al. · arXiv (Cornell University) · 2023 · 214 citations · Full text
Thomas Reps, Tim Teitelbaum · 1984 · 143 citations · Full text
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Microsoft (United States)
Redmond, United States
6 h-index
18 publications
632 citations
University of California, Davis
Davis, United States
3 h-index
5 publications
232 citations

Monash University
Melbourne, Australia
3 h-index
22 publications
2.9K citations

Cornell University
Ithaca, United States
3 h-index
6 publications
1.1K citations
University of California San Diego
San Diego, United States
2 h-index
2 publications
66 citations
Carnegie Mellon University
Pittsburgh, United States
2 h-index
3 publications
188 citations
The University of Tokyo
Tokyo, Japan
2 h-index
4 publications
72 citations
National University of Singapore
Singapore, Singapore
2 h-index
2 publications
101 citations
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2 publications · 390 citations
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Repository · 2 publications · 226 citations
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1 publications · 10 citations
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1 publications · 80 citations
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1 publications · 43 citations
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