most citedQ-PEFT: Query-dependent Parameter Efficient Fine-tuning for Text Reranking with Large Language Models

4 citations · 13 across the 14 of their papers we have counts for

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cs.CL2025

BookAsSumQA: An Evaluation Framework for Aspect-Based Book Summarization via Question Answering

Ryuhei Miyazato, Ting-Ruen Wei, Xuyang Wu +2

Aspect-based summarization aims to generate summaries that highlight specific aspects of a text, enabling more personalized and targeted summaries. However, its application to book…

cs.CL2025

A Survey on Feedback-based Multi-step Reasoning for Large Language Models on Mathematics

Ting-Ruen Wei, Haowei Liu, Xuyang Wu +1

Recent progress in large language models (LLM) found chain-of-thought prompting strategies to improve the reasoning ability of LLMs by encouraging problem solving through multiple…

cs.CL2025★ 1 cited

Does Reasoning Introduce Bias? A Study of Social Bias Evaluation and Mitigation in LLM Reasoning

Xuyang Wu, Jinming Nian, Ting-Ruen Wei +3

Recent advances in large language models (LLMs) have enabled automatic generation of chain-of-thought (CoT) reasoning, leading to strong performance on tasks such as math and code.…

cs.CL2024★ 2 cited

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…

cs.CL2024★ 1 cited

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…

cs.CL2024

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…