Papers by Georg Buscher
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. |
S3-DST: Structured Open-Domain Dialogue Segmentation and State Tracking in the Era of LLMs (2024.findings-acl)
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Sarkar Snigdha Sarathi Das, Chirag Shah, Mengting Wan, Jennifer Neville, Longqi Yang, Reid Andersen, Georg Buscher, Tara Safavi
| Challenge: | Dialogue state tracking (DST) was based on narrow task-oriented conversations . however, large language models have ushered in more flexible open-domain chat systems . |
| Approach: | They propose a method that combines dialogue segmentation and state tracking within open-domain dialogues to improve long context tracking. |
| Outcome: | The proposed method outperforms the state-of-the-art on open-domain dialogue datasets and publicly available datasets. |