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Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval

Paper recorded by Signals 4 on 2026-10-06 in cs.CL. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-06 on arXiv · recorded by Signals 4 on 2026-10-07

Category: cs.CL · 自然语言处理 · first seen 2026-10-07

Abstract

Generative retrieval trains a language model to generate the identifier of a relevant document. Recent work replaces the autoregressive decoder with diffusion, but changes identifiers, training recipe and decoding at once, so differences cannot be credited to the paradigm. On NQ320K and MS300K, we train autoregressive, masked-diffusion and block-diffusion models with residual-quantised, product-qu

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#310 most recent of 660 cs.CL papers we have recorded · ↑ newer: Holdout Best-of-N: Unbiased Evaluation and Its Cost · ↓ older: Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Stu
Cite this page: Disentangling Paradigm, Identifier, and Decoding in Generative Retrieval: the #310 most recent of 660 cs.CL papers we have recorded (as of 2026-10-06). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/disentangling-paradigm-identifier-and-decoding-in-generative-retrieval.html
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