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Ge Zhang

ACL 2026×2KDD 2026可解释性推理效率与系统智能体与工具对齐与偏好
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0 一作 · 3 篇 · ACL 2026 / KDD 2026

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2026 顶会论文

3
作者位次 #4/7ACL 2026Poster· Natural Language Processing

EA -Agent: A Structured Multi-Step Reasoning Agent for Entity Alignment

Yixuan Nan · Xixun Lin · Yanmin Shang · Ge Zhang · Zheng Fang · Fang Fang · Yanan Cao

摘要

Entity alignment (EA) aims to identify entities across different knowledge graphs (KGs) that refer to the same real-world object and plays a critical role in knowledge fusion and integration. Traditional EA methods mainly rely on knowledge representation learning, but their performance is often l...

推理可解释性效率与系统OpenReview 项目页
作者位次 #4/6ACL 2026Poster· Natural Language Processing

Do LLM s Know Tool Irrelevance? Demystifying Structural Alignment Bias in Tool Invocations

Yilong Liu · Xixun Lin · Pengfei Cao · Ge Zhang · Fang Fang · Yanan Cao

摘要

Large language models (LLMs) have demonstrated impressive capabilities in utilizing external tools. In practice, however, LLMs are often exposed to tools that are irrelevant to the user’s query, in which case the desired behavior is to refrain from invocations. In this work, we identify a widespr...

智能体与工具对齐与偏好可解释性OpenReview 项目页

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