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Zhanyu Wu

ACL 2026SIGIR 2026可解释性强化学习与后训练效率与系统对齐与偏好
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2 一作 · 2 篇 · ACL 2026 / SIGIR 2026

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2026 顶会论文

2
一作ACL 2026Poster· Natural Language Processing

Learning to Select: Query-Aware Adaptive Dimension Selection for Dense Retrieval

Zhanyu Wu · Richong Zhang · Zhijie Nie

摘要

Dense retrieval represents queries and documents as high-dimensional embeddings, but these representations can be redundant at the query level: for a given information need, only a subset of dimensions is consistently helpful for ranking. Prior work addresses this via pseudo-relevance feedback (P...

可解释性强化学习与后训练效率与系统OpenReview 项目页
一作SIGIR 2026Oral· Information Retrieval (Full)

Constructing Hard-Positive Query–Document Pairs for Dense Retrieval via Phrase Representativeness

Zhanyu Wu · Richong Zhang · Zhijie Nie

摘要

Dense retrieval usually fails in two ways: ranking non-relevant documents too high, or ranking relevant documents too low. We focus on the second case from the query side: documents that are genuinely relevant but receive low retrieval scores under certain query formulations, forming hard-positiv...

对齐与偏好可解释性强化学习与后训练OpenReview

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