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pro vyhledávání: '"Li,Zixuan"'
Autor:
Zuo, Yuxin, Jiang, Wenxuan, Liu, Wenxuan, Li, Zixuan, Bai, Long, Wang, Hanbin, Zeng, Yutao, Jin, Xiaolong, Guo, Jiafeng, Cheng, Xueqi
Empirical evidence suggests that LLMs exhibit spontaneous cross-lingual alignment. Our findings suggest that although LLMs also demonstrate promising cross-lingual alignment in Information Extraction, there remains significant imbalance across langua
Externí odkaz:
http://arxiv.org/abs/2411.04794
As current training data for Large Language Models (LLMs) are dominated by English corpus, they are English-centric and they present impressive performance on English reasoning tasks.\footnote{This paper primarily studies English-centric models, but
Externí odkaz:
http://arxiv.org/abs/2411.01141
Autor:
Li, Zixuan, Xiong, Jing, Ye, Fanghua, Zheng, Chuanyang, Wu, Xun, Lu, Jianqiao, Wan, Zhongwei, Liang, Xiaodan, Li, Chengming, Sun, Zhenan, Kong, Lingpeng, Wong, Ngai
We present UncertaintyRAG, a novel approach for long-context Retrieval-Augmented Generation (RAG) that utilizes Signal-to-Noise Ratio (SNR)-based span uncertainty to estimate similarity between text chunks. This span uncertainty enhances model calibr
Externí odkaz:
http://arxiv.org/abs/2410.02719
Adverse weather removal aims to restore clear vision under adverse weather conditions. Existing methods are mostly tailored for specific weather types and rely heavily on extensive labeled data. In dealing with these two limitations, this paper prese
Externí odkaz:
http://arxiv.org/abs/2409.19679
Autor:
Li, Zixuan, Shen, Pengfei, Sun, Hanxiao, Zhang, Zibo, Guo, Yu, Liu, Ligang, Yan, Ling-Qi, Marschner, Steve, Hasan, Milos, Wang, Beibei
Accurately rendering the appearance of fabrics is challenging, due to their complex 3D microstructures and specialized optical properties. If we model the geometry and optics of fabrics down to the fiber level, we can achieve unprecedented rendering
Externí odkaz:
http://arxiv.org/abs/2409.06368
Autor:
Chen, Guhong, Fan, Liyang, Gong, Zihan, Xie, Nan, Li, Zixuan, Liu, Ziqiang, Li, Chengming, Qu, Qiang, Ni, Shiwen, Yang, Min
In this paper, we present a simulation system called AgentCourt that simulates the entire courtroom process. The judge, plaintiff's lawyer, defense lawyer, and other participants are autonomous agents driven by large language models (LLMs). Our core
Externí odkaz:
http://arxiv.org/abs/2408.08089
Unifying Visual and Semantic Feature Spaces with Diffusion Models for Enhanced Cross-Modal Alignment
Image classification models often demonstrate unstable performance in real-world applications due to variations in image information, driven by differing visual perspectives of subject objects and lighting discrepancies. To mitigate these challenges,
Externí odkaz:
http://arxiv.org/abs/2407.18854
Autor:
Hu, Yuxuan, Tan, Minghuan, Zhang, Chenwei, Li, Zixuan, Liang, Xiaodan, Yang, Min, Li, Chengming, Hu, Xiping
Empathetic response generation is designed to comprehend the emotions of others and select the most appropriate strategies to assist them in resolving emotional challenges. Empathy can be categorized into cognitive empathy and affective empathy. The
Externí odkaz:
http://arxiv.org/abs/2407.21048
Autor:
Li, Zixuan
This paper explores the application of the GPT-4V(ision) large visual language model to autonomous driving in mining environments, where traditional systems often falter in understanding intentions and making accurate decisions during emergencies. GP
Externí odkaz:
http://arxiv.org/abs/2406.16817
Knowledge Base Question Answering (KBQA) has been a long-standing field to answer questions based on knowledge bases. Recently, the evolving dynamics of knowledge have attracted a growing interest in Temporal Knowledge Graph Question Answering (TKGQA
Externí odkaz:
http://arxiv.org/abs/2406.14191