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Bingguang Hao

ACL 2026×2社会与人本多模态评测与基准推理强化学习与后训练
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一作贡献30
核心/参与12
方向广度10
合作网络9
1 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #2/9ACL 2026Poster· Natural Language Processing

J anus MM : A Benchmark for Self-Deprecation Understanding in Real-World Multimodal Conversations

Xinyi Xu · Bingguang Hao · Yongyi Xiong · Zimo Chen · Xinchen Liu · Hongxin Guo · Xuelong Wang · Silin Zhou · Shihan Dou

摘要

Self-deprecation is a prevalent communicative strategy in human society, often using image-text interplay to express emotions and intentions. Despite self-deprecation is widespread in real-world conversations, the ability of multimodal large language models (MLLMs) to understand it remains undere...

社会与人本多模态评测与基准OpenReview 项目页
一作ACL 2026Findings· Findings of ACL

B alance SFT : Improving LLM Function Calling with Balanced Training Signals and Data Hardness

Bingguang Hao · Zengzhuang Xu · Maolin WANG · Yuntao Wen · Yicheng Chen · Cunyin Peng · Long Chen · an Jiaotong University) · Jinjie Gu · Chenyi Zhuang · Ji Zhang

摘要

While Supervised Fine-Tuning (SFT) is the prevailing method for equipping Large Language Models (LLMs) with function calling capabilities, its effectiveness is often compromised by two critical challenges: 1) Imbalanced Training Signals , where lengthy Chain-of-Thought (CoT) reasoning tokens domi...

推理强化学习与后训练数据与预训练OpenReview 项目页

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