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Shijian Wang

ACL 2026×2强化学习与后训练多语言与语言学对齐与偏好推理可解释性
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0 一作 · 2 篇 · ACL 2026

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2026 顶会论文

2
作者位次 #8/12ACL 2026Findings· Findings of ACL

A Multilingual Dataset and Empirical Validation for the Mutual Reinforcement Effect in Information Extraction

Chengguang Gan · Sunbowen Lee · Qingyu Yin · Yunhao Liang · Xinyang He · Hanjun Wei · Younghun Lim · Shijian Wang · Hexiang Huang · QingHao Zhang · Shiwen Ni · Tatsunori Mori

摘要

The Mutual Reinforcement Effect (MRE) describes a phenomenon in information extraction where word-level and sentence-level tasks can mutually improve each other when jointly modeled. While prior work has reported MRE in Japanese, its generality across languages and task settings has not been empi...

强化学习与后训练多语言与语言学对齐与偏好OpenReview 项目页
作者位次 #6/12ACL 2026Findings· Findings of ACL

C o TJ udger: A Graph-Driven Framework for Automatic Evaluation of Chain-of-Thought Efficiency and Redundancy in LRM s

Siyi Li · Jiajun Shi · Shiwen Ni · Ge Zhang · Shuaimin Li · Shijian Wang · Zhoufutu Wen · Yizhi Li · Hamid Alinejad-Rokny · JIAHENG LIU · Min Yang · Wenhao Huang

摘要

Large Reasoning Models (LRMs) have demonstrated strong performance by producing extended Chain-of-Thought (CoT) traces before answering. However, this paradigm often induces over-reasoning: redundant calculations and circular self-verification that increase computational cost without improving ou...

推理可解释性效率与系统OpenReview 项目页

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