Leveraging Evidence Priors for Robust Prompt Learning under Noisy Supervision in Vision-Language Models
Junnan Zou · Zhu Teng · Wei Zhang · Ming He · Jianping Fan
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Junnan Zou · Zhu Teng · Wei Zhang · Ming He · Jianping Fan
Junjie Ye · Caishuang Huang · Zhuohan Chen · Wenjie Fu · Chenyuan Yang · Leyi Yang · Yilong Wu · Peng Wang · Meng Zhou · Xiaolong Yang · Tao Gui · Qi Zhang · zhongchao shi · Jianping Fan · Xuanjing Huang
Instruction following refers to the ability of large language models (LLMs) to generate outputs that satisfy all specified constraints. Existing research has primarily focused on constraint categories, offering limited evaluation dimensions and little guidance for improving instruction-following...
Xuxian Hu · Zhu Teng · Wei Zhang · Ming He · Jianping Fan
Retrieval-Augmented Generation (RAG) systems are widely used to mitigate the stateless nature of Large Language Models (LLMs) in long-term and personalized interactions by incorporating external memory. However, existing approaches often prioritize memory organization, such as knowledge graphs, w...
Teng Shi · Weicong Qin · Weijie Yu · Xiao Zhang · Ming He · Jianping Fan · Jun Xu
Search and recommendation (S&R) are fundamental components of modern commercial platforms, enabling users to access and explore information efficiently. User behaviors in these scenarios reflect different aspects of user intent, providing an opportunity for joint modeling of S&R. However, effecti...