117 citations · 133 across the 24 of their papers we have counts for
5 papers · 1 filter
DiffuGR: Generative Document Retrieval with Diffusion Language Models
Xinpeng Zhao, Zhaochun Ren, Yukun Zhao +9
Generative retrieval (GR) reframes document retrieval as an end-to-end task of generating sequential document identifiers (DocIDs). Existing GR methods predominantly rely on left-t…
LLM Unlearning Should Be Form-Independent
Xiaotian Ye, Mengqi Zhang, Shu Wu
Large Language Model (LLM) unlearning aims to erase or suppress undesirable knowledge within the model, offering promise for controlling harmful or private information to prevent m…
Constrained Auto-Regressive Decoding Constrains Generative Retrieval
Shiguang Wu, Zhaochun Ren, Xin Xin +5
Generative retrieval seeks to replace traditional search index data structures with a single large-scale neural network, offering the potential for improved efficiency and seamless…
Open Problems and a Hypothetical Path Forward in LLM Knowledge Paradigms
Xiaotian Ye, Mengqi Zhang, Shu Wu
Knowledge is fundamental to the overall capabilities of Large Language Models (LLMs). The knowledge paradigm of a model, which dictates how it encodes and utilizes knowledge, signi…
UIPE: Enhancing LLM Unlearning by Removing Knowledge Related to Forgetting Targets
Wenyu Wang, Mengqi Zhang, Xiaotian Ye +3
Large Language Models (LLMs) inevitably acquire harmful information during training on massive datasets. LLM unlearning aims to eliminate the influence of such harmful information…