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Zhaocheng Du

KDD 2026×3AAAI 2026×2ACL 2026×2ICLR 2026SIGIR 2026检索与知识可解释性智能体与工具对齐与偏好推理
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0 一作 · 9 篇 · KDD 2026 / AAAI 2026 / ACL 2026 / ICLR 2026 / SIGIR 2026

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2026 顶会论文

9
作者位次 #2/5ACL 2026Poster· Natural Language Processing

From ID to LLM : Rethinking Representation Learning for Recommendation

Song-Li Wu · Zhaocheng Du · Weinan Gan · Jingyi Wang · Xianquan Wang

摘要

Recent studies indicate a fundamental incompatibility between ID representations and language model (LM) representations, as they capture behavioral and semantic spaces respectively. This mismatch leads LM representations to consistently underperform ID representations in recommendation tasks. In...

对齐与偏好推理可解释性OpenReview 项目页
作者位次 #2/4ACL 2026Findings· Findings of ACL

C a PE dit: Capability-Preserving Lifelong Knowledge Editing For Language Models

Song-Li Wu · Zhaocheng Du · Xianquan Wang · Jingyi Wang

摘要

Lifelong knowledge editing (LKE) aims to incrementally correct factual inaccuracies in large language models (LLMs), but sequential edits can lead to substantial degradation of capabilities. Existing approaches primarily rely on static parameter regularization, which restricts knowledge integrati...

检索与知识多模态OpenReview 项目页
作者位次 #6/10SIGIR 2026Oral· Information Retrieval (Full)

FollowTable: A Benchmark for Instruction-Following Table Retrieval

Rihui Jin · Yuchen Lu · Ting Zhang · Jun Wang · Kuicai Dong · Zhaocheng Du · Dongping Liu · Gang Wang · Yong Liu · Guilin Qi

摘要

Table Retrieval (TR) has traditionally been formulated as an ad-hoc retrieval problem, where relevance is primarily determined by topical semantic similarity. With the growing adoption of LLMs-based agentic systems, access to structured data is increasingly instruction-driven, where relevance is...

评测与基准智能体与工具可解释性OpenReview

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