Answer Generation
Answer Generation is the computational process within question answering systems whereby a system produces a natural language response to a given query, distinguished from related processes by its focus on synthesizing linguistically coherent and contextually accurate answers rather than retrieving or selecting pre-existing content.
611
Publications
34.6K
Citations
1.9K
Authors
478
Institutions
Publications per year
2017–2026
371
Parents
611
GPT-3: Its Nature, Scope, Limits, and Consequences
Luciano Floridi, Massimo Chiriatti · Minds and Machines · 2020 · 2.2K citations · Full text
Detecting hallucinations in large language models using semantic entropy
Sebastian Farquhar, Jannik Kossen, Lorenz Kuhn et al. · Nature · 2024 · 626 citations · Full text
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478

Peking University
Beijing, China
18 h-index
51 publications
2.9K citations
Carnegie Mellon University
Pittsburgh, United States
15 h-index
43 publications
3K citations
Chinese University of Hong Kong
Hong Kong, Hong Kong
13 h-index
40 publications
2K citations
Google (United States)
Mountain View, United States
13 h-index
69 publications
6.8K citations
Microsoft Research (United Kingdom)
Cambridge, United Kingdom
12 h-index
27 publications
1.5K citations

Chinese Academy of Sciences
Beijing, China
9 h-index
25 publications
1.6K citations
Microsoft (United States)
Redmond, United States
9 h-index
27 publications
3.7K citations
University of Washington
Seattle, United States
8 h-index
14 publications
627 citations
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Repository · 85 publications · 4.8K citations
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Conference · 18 publications · 847 citations
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10 publications · 307 citations
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Conference · 8 publications · 849 citations
8
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TACL · 5 publications · 272 citations
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