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Dong Fang

ACL 2026×2ICLR 2026KDD 2026推理强化学习与后训练安全多模态可解释性
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0 一作 · 4 篇 · ACL 2026 / ICLR 2026 / KDD 2026

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2026 顶会论文

4
作者位次 #3/10ACL 2026Poster· Natural Language Processing

Vision-Language Introspection: Mitigating Overconfident Hallucinations in MLLM s via Interpretable Bi-Causal Steering

Shuliang Liu · Songbo Yang · Dong Fang · Sihang Jia · Yuqi Tang · Lingfeng Su · Ruoshui Peng · Yibo Yan · Xin Zou · Xuming Hu

摘要

Object hallucination critically undermines the reliability of Multimodal Large Language Models (MLLMs), often stemming from a fundamental failure in cognitive introspection—where models blindly trust linguistic priors over specific visual evidence. Existing mitigations remain limited: contrastive...

多模态可解释性对齐与偏好OpenReview 项目页
作者位次 #8/10ACL 2026Findings· Findings of ACL

L earn A lign: Data Selection for LLM Reinforcement Learning with Improved Gradient Alignment

Shipeng Li · Zhiqin Yang · Shikun Li · Xiaobo Xia · Hengyu Liu · Xinghua Zhang · Gaode Chen · Dong Fang · Ying Tai · Zhe Peng

摘要

Reinforcement learning with verifiable rewards (RLVR) has become a key technique for enhancing LLMs’ reasoning abilities, yet its data inefficiency remains a major bottleneck. To address this critical yet challenging issue, we present a novel gradient-alignment-based method, named LearnAlign, whi...

强化学习与后训练推理数据与预训练OpenReview 项目页

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