5 citations · 6 across the 4 of their papers we have counts for
4 papers · 1 filter
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,…
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…
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…
Clarify When Necessary: Resolving Ambiguity Through Interaction with LMs
Michael J. Q. Zhang, Eunsol Choi
Resolving ambiguities through interaction is a hallmark of natural language, and modeling this behavior is a core challenge in crafting AI assistants. In this work, we study such b…