7 citations · 11 across the 7 of their papers we have counts for
7 papers
FactNet: A Billion-Scale Knowledge Graph for Multilingual Factual Grounding
Yingli Shen, Wen Lai, Jie Zhou +7
Large language models hallucinate factual claims and struggle to ground their outputs in retrievable evidence, particularly in non-English languages. Existing resources impose a tr…
I Could've Asked That: Reformulating Unanswerable Questions
Wenting Zhao, Ge Gao, Claire Cardie +1
When seeking information from unfamiliar documents, users frequently pose questions that cannot be answered by the documents. While existing large language models (LLMs) identify t…
Aligning LLM Agents by Learning Latent Preference from User Edits
Ge Gao, Alexey Taymanov, Eduardo Salinas +2
We study interactive learning of LLM-based language agents based on user edits made to the agent's output. In a typical setting such as writing assistants, the user interacts with…
Policy-Gradient Training of Language Models for Ranking
Ge Gao, Jonathan D. Chang, Claire Cardie +2
Text retrieval plays a crucial role in incorporating factual knowledge for decision making into language processing pipelines, ranging from chat-based web search to question answer…
Continually Improving Extractive QA via Human Feedback
Ge Gao, Hung-Ting Chen, Yoav Artzi +1
We study continually improving an extractive question answering (QA) system via human user feedback. We design and deploy an iterative approach, where information-seeking users ask…
Simulating Bandit Learning from User Feedback for Extractive Question Answering
Ge Gao, Eunsol Choi, Yoav Artzi
We study learning from user feedback for extractive question answering by simulating feedback using supervised data. We cast the problem as contextual bandit learning, and analyze…
Neural Metaphor Detection in Context
Ge Gao, Eunsol Choi, Yejin Choi +1
We present end-to-end neural models for detecting metaphorical word use in context. We show that relatively standard BiLSTM models which operate on complete sentences work well in…