39 citations · 117 across the 22 of their papers we have counts for
10 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…
MADS: Model-Aware Diverse Core Set Selection for Instruction Tuning
Yi Bai, Wenhao Zhang, Yao Chen +3
Instruction fine-tuning is employed to enhance the instruction-following ability of large language models (LLMs). As the amount of instruction fine-tuning data increases, selecting…
Integrating Chain-of-Thought into Generative Retrieval: A Preliminary Study
Wenhao Zhang, Ruihao Yu, Yi Bai +2
While generative retrieval (GR) demonstrates competitive performance on standard retrieval benchmarks, existing approaches directly map queries to document identifiers (docids) wit…
Federated User Behavior Modeling for Privacy-Preserving LLM Recommendation
Lei Guo, Hongyun Yang, Pengjie Ren +3
Large Language Models have shown great success in recommender systems. However, the limited and sparse nature of user data often restricts the LLM's ability to effectively model be…
Uncovering Context Reliance in Unstructured Knowledge Editing
Zisheng Zhou, Mengqi Zhang, Shiguang Wu +4
Editing Large language models (LLMs) with real-world, unstructured knowledge is essential for correcting and updating their internal parametric knowledge. In this work, we revisit…