Papers by Scott Counts
Interpretable User Satisfaction Estimation for Conversational Systems with Large Language Models (2024.acl-long)
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Ying-Chun Lin, Jennifer Neville, Jack Stokes, Longqi Yang, Tara Safavi, Mengting Wan, Scott Counts, Siddharth Suri, Reid Andersen, Xiaofeng Xu, Deepak Gupta, Sujay Kumar Jauhar, Xia Song, Georg Buscher, Saurabh Tiwary, Brent Hecht, Jaime Teevan
| Challenge: | Existing approaches to user satisfaction estimation are hard to interpret and lack generalizable patterns. |
| Approach: | They propose to use supervised prompting to extract interpretable user satisfaction signals from natural language utterances to tailor an LLM to USE using labeled examples. |
| Outcome: | The proposed method extracts interpretable signals of user satisfaction from natural language utterances more effectively than embedding-based approaches. |