Human Feedback Analysis
Human Feedback Analysis is a scholarly methodological framework within Human-Centered Data Analysis that systematically captures, interprets, and integrates qualitative and quantitative input from human users to refine data models, processes, or systems, distinguishing itself by its focus on iterative human-in-the-loop validation and improvement.
44
Publications
6.6K
Citations
287
Authors
62
Institutions
Publications per year
2017–2026
33
Parents
44
Training language models to follow instructions with human feedback
Long Ouyang, Jeff Wu, Xu Jiang et al. · arXiv (Cornell University) · 2022 · 4.3K citations · Full text
Larger and more instructable language models become less reliable
Lexin Zhou, Wout Schellaert, Fernando Martínez‐Plumed et al. · Nature · 2024 · 157 citations · Full text
Discovering Language Model Behaviors with Model-Written Evaluations
Ethan Perez, Sam Ringer, Kamilė Lukošiūtė et al. · 2023 · 139 citations · Full text
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Georgia Institute of Technology
Atlanta, United States
3 h-index
6 publications
180 citations
University of Cambridge
Cambridge, United Kingdom
2 h-index
3 publications
363 citations
University at Buffalo, State University of New York
Buffalo, United States
1 h-index
3 publications
48 citations
Google (United States)
Mountain View, United States
1 h-index
14 publications
686 citations

Brandeis University
Waltham, United States
1 h-index
1 publications
49 citations
Rutgers, The State University of New Jersey
New Brunswick, United States
1 h-index
5 publications
90 citations
Technology Holding (United States)
Salt Lake City, United States
1 h-index
1 publications
91 citations

Radboud University Nijmegen
Nijmegen, The Netherlands
1 h-index
3 publications
315 citations
1–8 of 62
Repository · 10 publications · 4.8K citations
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1 publications · 157 citations
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RTP · 1 publications · 48 citations
1
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1 publications · 33 citations
1
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1 publications · 19 citations
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