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20242026
most citedHPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task

1 citations · 1 across the 1 of their papers we have counts for

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14 papers

cs.CV20261 cited

HPE-CogVLM: Advancing Vision Language Models with a Head Pose Grounding Task

Yu Tian, Tianqi Shao, Tsukasa Demizu +2

Head pose estimation (HPE) requires a sophisticated understanding of 3D spatial relationships to generate precise yaw, pitch, and roll angles. Previous HPE models, primarily CNN-ba…

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

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.CL2025

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…

cs.IR2025

RaCT: Ranking-aware Chain-of-Thought Optimization for LLMs

Haowei Liu, Xuyang Wu, Guohao Sun +2

In information retrieval, large language models (LLMs) have demonstrated remarkable potential in text reranking tasks by leveraging their sophisticated natural language understandi…

cs.CL2025

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…