4 citations · 11 across the 11 of their papers we have counts for
5 papers · 1 filter
Does RAG Introduce Unfairness in LLMs? Evaluating Fairness in Retrieval-Augmented Generation Systems
Xuyang Wu, Shuowei Li, Hsin-Tai Wu +2
Retrieval-Augmented Generation (RAG) has recently gained significant attention for its enhanced ability to integrate external knowledge sources into open-domain question answering…
Table Transformers for Imputing Textual Attributes
Ting-Ruen Wei, Yuan Wang, Yoshitaka Inoue +2
Missing data in tabular dataset is a common issue as the performance of downstream tasks usually depends on the completeness of the training dataset. Previous missing data imputati…
Passage-specific Prompt Tuning for Passage Reranking in Question Answering with Large Language Models
Xuyang Wu, Zhiyuan Peng, Krishna Sravanthi Rajanala Sai +2
Effective passage retrieval and reranking methods have been widely utilized to identify suitable candidates in open-domain question answering tasks, recent studies have resorted to…
Evaluating Fairness in Large Vision-Language Models Across Diverse Demographic Attributes and Prompts
Xuyang Wu, Yuan Wang, Hsin-Tai Wu +2
Large vision-language models (LVLMs) have recently achieved significant progress, demonstrating strong capabilities in open-world visual understanding. However, it is not yet clear…
Q-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models
Zhiyuan Peng, Xuyang Wu, Qifan Wang +2
Parameter Efficient Fine-Tuning (PEFT) methods have been extensively utilized in Large Language Models (LLMs) to improve the down-streaming tasks without the cost of fine-tuing the…