84 citations · 194 across the 103 of their papers we have counts for
14 papers · 2 filters
RAG-Star: Enhancing Deliberative Reasoning with Retrieval Augmented Verification and Refinement
Jinhao Jiang, Jiayi Chen, Junyi Li +5
Existing large language models (LLMs) show exceptional problem-solving capabilities but might struggle with complex reasoning tasks. Despite the successes of chain-of-thought and t…
Think&Cite: Improving Attributed Text Generation with Self-Guided Tree Search and Progress Reward Modeling
Junyi Li, Hwee Tou Ng
Despite their outstanding capabilities, large language models (LLMs) are prone to hallucination and producing factually incorrect information. This challenge has spurred efforts in…
QuaLLM-Health: An Adaptation of an LLM-Based Framework for Quantitative Data Extraction from Online Health Discussions
Ramez Kouzy, Roxanna Attar-Olyaee, Michael K. Rooney +3
Health-related discussions on social media like Reddit offer valuable insights, but extracting quantitative data from unstructured text is challenging. In this work, we present an…
LLMBox: A Comprehensive Library for Large Language Models
Tianyi Tang, Yiwen Hu, Bingqian Li +22
To facilitate the research on large language models (LLMs), this paper presents a comprehensive and unified library, LLMBox, to ease the development, use, and evaluation of LLMs. T…
Learning to Refine with Fine-Grained Natural Language Feedback
Manya Wadhwa, Xinyu Zhao, Junyi Jessy Li +1
Recent work has explored the capability of large language models (LLMs) to identify and correct errors in LLM-generated responses. These refinement approaches frequently evaluate w…
Detection and Measurement of Syntactic Templates in Generated Text
Chantal Shaib, Yanai Elazar, Junyi Jessy Li +1
Recent work on evaluating the diversity of text generated by LLMs has focused on word-level features. Here we offer an analysis of syntactic features to characterize general repeti…
Do they mean 'us'? Interpreting Referring Expressions in Intergroup Bias
Venkata S Govindarajan, Matianyu Zang, Kyle Mahowald +2
The variations between in-group and out-group speech (intergroup bias) are subtle and could underlie many social phenomena like stereotype perpetuation and implicit bias. In this p…
Robo-Instruct: Simulator-Augmented Instruction Alignment For Finetuning Code LLMs
Zichao Hu, Junyi Jessy Li, Arjun Guha +1
Code LLMs have shown promising results with converting tasks in natural language to programs that can be executed by service robots. We are interested in finetuning small, speciali…
Which questions should I answer? Salience Prediction of Inquisitive Questions
Yating Wu, Ritika Mangla, Alexandros G. Dimakis +2
Inquisitive questions -- open-ended, curiosity-driven questions people ask as they read -- are an integral part of discourse processing (Kehler and Rohde, 2017; Onea, 2016) and com…
Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided
Hongli Zhan, Allen Zheng, Yoon Kyung Lee +3
Large language models (LLMs) have offered new opportunities for emotional support, and recent work has shown that they can produce empathic responses to people in distress. However…
Large Language Models Produce Responses Perceived to be Empathic
Yoon Kyung Lee, Jina Suh, Hongli Zhan +2
Large Language Models (LLMs) have demonstrated surprising performance on many tasks, including writing supportive messages that display empathy. Here, we had these models generate…
FactPICO: Factuality Evaluation for Plain Language Summarization of Medical Evidence
Sebastian Antony Joseph, Lily Chen, Jan Trienes +5
Plain language summarization with LLMs can be useful for improving textual accessibility of technical content. But how factual are these summaries in a high-stakes domain like medi…
The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models
Junyi Li, Jie Chen, Ruiyang Ren +4
In the era of large language models (LLMs), hallucination (i.e., the tendency to generate factually incorrect content) poses great challenge to trustworthy and reliable deployment…
InfoLossQA: Characterizing and Recovering Information Loss in Text Simplification
Jan Trienes, Sebastian Joseph, Jörg Schlötterer +5
Text simplification aims to make technical texts more accessible to laypeople but often results in deletion of information and vagueness. This work proposes InfoLossQA, a framework…