21 citations · 70 across the 19 of their papers we have counts for
7 papers · 1 filter
Token-Level Credit Assignment Optimization for Generative Document Retrieval
Xinpeng Zhao, Yang Liu, Ran Chen +6
Generative retrieval models perform document retrieval by autoregressively generating document identifiers (DocIDs). This process naturally forms a sequential decision problem, i.e…
Conflict-Aware Retriever Editing for Knowledge Injection Attacks on LLM-Based RAG Systems
Xinru Liu, Xianglong Zhang, Di Cai +3
Injecting malicious knowledge into retrieval-augmented generation (RAG) systems can manipulate retrieved evidence and mislead downstream generation, posing a serious security threa…
Do Generative Recommenders Deepen the Information Cocoon? A Closed-Loop Simulation with LLM-powered User Simulators
Jiyuan Yang, Gengxin Sun, Mengqi Zhang +5
Recommender systems alleviate information overload, yet repeated feedback between recommendations and user interactions can reinforce existing preferences and narrow users' exposur…
Beyond Imitation: Reflective On-Policy Self-Distillation for LLM Reasoning
Ziqi Zhao, Xinyu Ma, Liu Yang +6
On-policy self-distillation (OPSD) improves the reasoning capabilities of large language models (LLMs) by providing dense token-level supervision for on-policy rollouts. However, e…
Cold-Starts in Generative Recommendation: A Reproducibility Study
Zhen Zhang, Jujia Zhao, Xinyu Ma +3
Cold-start recommendation remains a central challenge in dynamic, open-world platforms, requiring models to recommend for newly registered users (user cold-start) and to recommend…
Model Editing for New Document Integration in Generative Information Retrieval
Zhen Zhang, Zihan Wang, Xinyu Ma +6
Generative retrieval (GR) reformulates the Information Retrieval (IR) task as the generation of document identifiers (docIDs). Despite its promise, existing GR models exhibit poor…
Reinforced Efficient Reasoning via Semantically Diverse Exploration
Ziqi Zhao, Zhaochun Ren, Jiahong Zou +9
Reinforcement learning with verifiable rewards (RLVR) has proven effective in enhancing the reasoning of large language models (LLMs). Monte Carlo Tree Search (MCTS)-based extensio…