59 citations · 240 across the 38 of their papers we have counts for
8 papers · 1 filter
Knowledge-Aware Query Expansion with Large Language Models for Textual and Relational Retrieval
Yu Xia, Junda Wu, Sungchul Kim +4
Large language models (LLMs) have been used to generate query expansions augmenting original queries for improving information search. Recent studies also explore providing LLMs wi…
Causal Discovery in Semi-Stationary Time Series
Shanyun Gao, Raghavendra Addanki, Tong Yu +2
Discovering causal relations from observational time series without making the stationary assumption is a significant challenge. In practice, this challenge is common in many areas…
Learning to Reduce: Towards Improving Performance of Large Language Models on Structured Data
Younghun Lee, Sungchul Kim, Ryan A. Rossi +2
Large Language Models (LLMs) have been achieving competent performance on a wide range of downstream tasks, yet existing work shows that inference on structured data is challenging…
Hallucination Diversity-Aware Active Learning for Text Summarization
Yu Xia, Xu Liu, Tong Yu +5
Large Language Models (LLMs) have shown propensity to generate hallucinated outputs, i.e., texts that are factually incorrect or unsupported. Existing methods for alleviating hallu…
SciCapenter: Supporting Caption Composition for Scientific Figures with Machine-Generated Captions and Ratings
Ting-Yao Hsu, Chieh-Yang Huang, Shih-Hong Huang +5
Crafting effective captions for figures is important. Readers heavily depend on these captions to grasp the figure's message. However, despite a well-developed set of AI technologi…
Which LLM to Play? Convergence-Aware Online Model Selection with Time-Increasing Bandits
Yu Xia, Fang Kong, Tong Yu +4
Web-based applications such as chatbots, search engines and news recommendations continue to grow in scale and complexity with the recent surge in the adoption of LLMs. Online mode…
Learning to Reduce: Optimal Representations of Structured Data in Prompting Large Language Models
Younghun Lee, Sungchul Kim, Tong Yu +2
Large Language Models (LLMs) have been widely used as general-purpose AI agents showing comparable performance on many downstream tasks. However, existing work shows that it is cha…
Self-Debiasing Large Language Models: Zero-Shot Recognition and Reduction of Stereotypes
Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow +6
Large language models (LLMs) have shown remarkable advances in language generation and understanding but are also prone to exhibiting harmful social biases. While recognition of th…