Papers by Steve Johnson
Learning LLM Preference over Intra-Dialogue Pairs: A Framework for Utterance-level Understandings (2025.naacl-industry)
Copied to clipboard
Xuanqing Liu, Luyang Kong, Wei Niu, Afshin Khashei, Belinda Zeng, Steve Johnson, Jon Jay, Davor Golac, Matt Pope
| Challenge: | Large language models (LLMs) have demonstrated remarkable capabilities in handling complex dialogue tasks without requiring use case-specific fine-tuning. |
| Approach: | They propose a framework that combines the scalability of LLM-generated labels with the precision of human annotations to achieve higher speed and accuracy comparable to larger models. |
| Outcome: | The proposed framework significantly improves accuracy across utterance-level dialogue tasks, including sentiment detection (over 2%), dialogue act classification (over 1.5%), etc. |