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Grace Hui Yang

ACL 2026×2KDD 2026智能体与工具检索与知识社会与人本强化学习与后训练可解释性
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合作网络6
1 一作 · 3 篇 · ACL 2026 / KDD 2026

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2026 顶会论文

3
作者位次 #2/2ACL 2026Poster· Natural Language Processing

YIELD : A Large-Scale Dataset and Evaluation Framework for Information Elicitation Agents

Victor De Lima · Grace Hui Yang

摘要

Most conversational agents (CAs) are designed to satisfy user needs through user-driven interactions. However, many real-world settings, such as academic interviewing, judicial proceedings, and journalistic investigations, involve broader institutional decision-making processes and require agents...

检索与知识社会与人本智能体与工具OpenReview 项目页
作者位次 #4/5ACL 2026Findings· Findings of ACL

Dual Hierarchical Dialogue Policy Learning for Legal Inquisitive Conversational Agents

Xubo Lin · Zezhi Deng · Shihao Wang · Grace Hui Yang · Yang Deng

摘要

Most existing dialogue systems are user-driven, primarily designed to fulfill user requests. However, in many critical real-world scenarios, a conversational agent must proactively extract information to achieve its own objectives rather than merely respond. To address this gap, we introduce Inqu...

强化学习与后训练智能体与工具可解释性OpenReview 项目页

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