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Qin Chen

ACL 2026×4AAAI 2026CVPR 2026ICLR 2026推理检索与知识多模态可解释性评测与基准
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0 一作 · 7 篇 · ACL 2026 / AAAI 2026 / CVPR 2026 / ICLR 2026

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2026 顶会论文

7
作者位次 #3/5ACL 2026Poster· Natural Language Processing

T am E dit: Trajectory-Aware Meta-Learning for Specificity-Preserving Continual Knowledge Editing

Shiqiang Tian · Cheng Ding · Qin Chen · Jie Zhou · Liang He

摘要

Knowledge editing is a promising method for updating Large Language Models efficiently. However, previous studies often suffer from poor specificity in continual editing, as they typically focus on single edits or preventing knowledge forgetting. To address this, we propose TamEdit, a trajectory-...

强化学习与后训练检索与知识效率与系统OpenReview 项目页
作者位次 #8/15ACL 2026Poster· Natural Language Processing

A Survey of Inductive Reasoning for Large Language Models

Kedi Chen · Dezhao Ruan · Yuhao Dan · Yaoting Wang · Siyu Yan · Xuecheng Wu · Yinqi Zhang · Qin Chen · Jie Zhou · Liang He · Biqing Qi · Linyang Li · Qipeng Guo · Xiaoming Shi · Wei Zhang

摘要

Reasoning is an important task for large language models (LLMs). Among all the reasoning paradigms, inductive reasoning is one of the basic types, which is characterized by its particular-to-general thinking process and the non-uniqueness of its answers. The inductive mode is crucial for knowledg...

评测与基准检索与知识推理OpenReview 项目页
作者位次 #10/13ACL 2026Findings· Findings of ACL

P sych E val: A Multi-Session and Multi-Therapy Benchmark for High-Realism AI Psychological Counselor

Qianjun Pan · Junyi Wang · Jie Zhou · Yutao Yang · Junsong Li · Kaiyin Xu · Yougen Zhou · Yihan Li · JingYuan Zhao · Qin Chen · Ningning Zhou · Kai Chen · Liang He

摘要

To develop a reliable AI for psychological assessment, we introduce PsychEval, a multi-session, multi-therapy, and highly realistic benchmark designed to address three key challenges: 1) Can we train a highly realistic AI counselor? Realistic counseling is a longitudinal task requiring sustained...

推理评测与基准数据与预训练OpenReview 项目页
作者位次 #3/7ACL 2026Findings· Findings of ACL

LLM - KT : Enhancing Large Language Models with Knowledge Tracing via Multi-Level Plug-and-Play Alignment

Ziwei Wang · Jie Zhou · Qin Chen · Bo Jiang · Qingchun Bai · Liang Dou · Liang He

摘要

Knowledge Tracing (KT) is a pivotal task in personalized education, aiming to predict students’ future performance based on their historical interactions. While prior work has focused on learning behavioral sequences using question IDs or surface-level textual features, these methods often fail t...

对齐与偏好可解释性推理OpenReview 项目页

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