24 citations · 51 across the 13 of their papers we have counts for
12 papers · 1 filter
Contrastive Learning to Improve Retrieval for Real-world Fact Checking
Aniruddh Sriram, Fangyuan Xu, Eunsol Choi +1
Recent work on fact-checking addresses a realistic setting where models incorporate evidence retrieved from the web to decide the veracity of claims. A bottleneck in this pipeline…
KIWI: A Dataset of Knowledge-Intensive Writing Instructions for Answering Research Questions
Fangyuan Xu, Kyle Lo, Luca Soldaini +3
Large language models (LLMs) adapted to follow user instructions are now widely deployed as conversational agents. In this work, we examine one increasingly common instruction-foll…
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…
Development and Evaluation of Three Chatbots for Postpartum Mood and Anxiety Disorders
Xuewen Yao, Miriam Mikhelson, S. Craig Watkins +3
In collaboration with Postpartum Support International (PSI), a non-profit organization dedicated to supporting caregivers with postpartum mood and anxiety disorders, we developed…
Concise Answers to Complex Questions: Summarization of Long-form Answers
Abhilash Potluri, Fangyuan Xu, Eunsol Choi
Long-form question answering systems provide rich information by presenting paragraph-level answers, often containing optional background or auxiliary information. While such compr…
A Critical Evaluation of Evaluations for Long-form Question Answering
Fangyuan Xu, Yixiao Song, Mohit Iyyer +1
Long-form question answering (LFQA) enables answering a wide range of questions, but its flexibility poses enormous challenges for evaluation. We perform the first targeted study o…