3 papers
cs.CL2025
User Feedback in Human-LLM Dialogues: A Lens to Understand Users But Noisy as a Learning Signal
Yuhan Liu, Michael J. Q. Zhang, Eunsol Choi
Once language models (LMs) are deployed, they can interact with users long-term, ideally evolving based on their feedback. Asking for direct user feedback can be disruptive; thus,…
cs.CL2025
Improving LLM-as-a-Judge Inference with the Judgment Distribution
Victor Wang, Michael J. Q. Zhang, Eunsol Choi
Using language models to scalably approximate human preferences on text quality (LLM-as-a-judge) has become a standard practice applicable to many tasks. A judgment is often extrac…
cs.CL2025
Modeling Future Conversation Turns to Teach LLMs to Ask Clarifying Questions
Michael J. Q. Zhang, W. Bradley Knox, Eunsol Choi
Large language models (LLMs) must often respond to highly ambiguous user requests. In such cases, the LLM's best response may be to ask a clarifying question to elicit more informa…