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Jianping Fan

ACL 2026×2ICML 2026SIGIR 2026检索与知识评测与基准对齐与偏好多模态代码
新星分资深
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核心/参与14
方向广度16
合作网络6
0 一作 · 4 篇 · ACL 2026 / ICML 2026 / SIGIR 2026

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2026 顶会论文

4
作者位次 #5/5ICML 2026Poster· Deep Learning / Large Language Models

Leveraging Evidence Priors for Robust Prompt Learning under Noisy Supervision in Vision-Language Models

Junnan Zou · Zhu Teng · Wei Zhang · Ming He · Jianping Fan

多模态OpenReview
作者位次 #14/15ACL 2026Findings· Findings of ACL

M ul D im IF : A Multi-Dimensional Constraint Framework for Evaluating and Improving Instruction Following in Large Language Models

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

摘要

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...

评测与基准检索与知识代码OpenReview 项目页
作者位次 #5/5ACL 2026Findings· Findings of ACL

Q uery L ink: Leveraging Query-Memory Alignment for Long-Term Reasoning in LLM Agents

Xuxian Hu · Zhu Teng · Wei Zhang · Ming He · Jianping Fan

摘要

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...

检索与知识对齐与偏好评测与基准OpenReview 项目页
作者位次 #6/7SIGIR 2026Oral· Information Retrieval (Full)

Mining Informative Interests via Latent Cross Reasoning for Search Enhanced Recommendation

Teng Shi · Weicong Qin · Weijie Yu · Xiao Zhang · Ming He · Jianping Fan · Jun Xu

摘要

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...

检索与知识推理对齐与偏好OpenReview

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