6 papers
Computed, not Memorized: Bit-Factorized Identifiers for Efficient Generative Retrieval
I-Fan Lin, Zhen Zhang, Faegheh Hasibi +1
Generative retrieval is known for its efficient end-to-end indexing and retrieval. In particular, numeric Document Identifiers (DocIDs) are generally more efficient because they ar…
How Much Dense Attention is Necessary? Oracle-Guided Sparse Prefill for Full/GQA Layers in Hybrid Long-Context Models
Hongxing Wang, Harenome Razanajato, Zhen Zhang +2
Long-context prefill remains expensive because full/GQA layers still score the historical sequence, even in hybrid models with local, sparse, linear, or recurrent components. We st…
GIRL-DETR: Gradient-Isolated Reinforcement Learning for Video Moment Retrieval
Shihang Zhang, Mingjin Kuai, Ye Wei +2
Video Moment Retrieval (VMR) task requires accurately localizing temporal boundaries aligned with natural language queries, but many models suffer from a misalignment between conti…
Exploring Training and Inference Scaling Laws in Generative Retrieval
Hongru Cai, Yongqi Li, Ruifeng Yuan +4
Generative retrieval reformulates retrieval as an autoregressive generation task, where large language models (LLMs) generate target documents directly from a query. As a novel par…
Unsupervised Query Routing for Retrieval Augmented Generation
Feiteng Mu, Liwen Zhang, Yong Jiang +4
Query routing for retrieval-augmented generation aims to assign an input query to the most suitable search engine. Existing works rely heavily on supervised datasets that require e…
Distillation Enhanced Generative Retrieval
Yongqi Li, Zhen Zhang, Wenjie Wang +3
Generative retrieval is a promising new paradigm in text retrieval that generates identifier strings of relevant passages as the retrieval target. This paradigm leverages powerful…