@inproceedings{kim-etal-2026-d3,
title = "D3: Dynamic Docid Decoding for Multi-Intent Generative Retrieval",
author = "Kim, Jaeyoung and
Lee, Dohyeon and
Hong, Soona and
Hwang, Seung-won",
editor = {Matusevych, Yevgen and
Eryi{\u{g}}it, G{\"u}l{\c{s}}en and
Aletras, Nikolaos},
booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 5: Industry Track)",
month = mar,
year = "2026",
address = "Rabat, Morocco",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.eacl-industry.58/",
pages = "789--800",
ISBN = "979-8-89176-384-5",
abstract = "Generative Retrieval (GR) maps queries to documents by generating discrete identifiers (DocIDs).However, offline DocID assignment and constrained decoding often prevent GR from capturing query-specific intent, especially when documents express multiple or unseen intents (i.e., intent misalignment).We introduce Dynamic Docid Decoding (D3), an inference-time mechanism that adaptively refines DocIDs through delayed, query-informed identifier expansion.D3 uses (a) verification to detect intent misalignment and (b) dynamic decoding to extend DocIDs with query-aligned tokens, even those absent from the pre-indexed vocabulary, enabling plug-and-play DocID expansion beyond the static vocabulary while adding minimal overhead.Experiments on NQ320k and MS-MARCO show that D3 consistently improves retrieval accuracy, especially on unseen and multi-intent documents, across various GR models, including a +2.4{\%}p nDCG@10 gain on the state-of-the-art model."
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<abstract>Generative Retrieval (GR) maps queries to documents by generating discrete identifiers (DocIDs).However, offline DocID assignment and constrained decoding often prevent GR from capturing query-specific intent, especially when documents express multiple or unseen intents (i.e., intent misalignment).We introduce Dynamic Docid Decoding (D3), an inference-time mechanism that adaptively refines DocIDs through delayed, query-informed identifier expansion.D3 uses (a) verification to detect intent misalignment and (b) dynamic decoding to extend DocIDs with query-aligned tokens, even those absent from the pre-indexed vocabulary, enabling plug-and-play DocID expansion beyond the static vocabulary while adding minimal overhead.Experiments on NQ320k and MS-MARCO show that D3 consistently improves retrieval accuracy, especially on unseen and multi-intent documents, across various GR models, including a +2.4%p nDCG@10 gain on the state-of-the-art model.</abstract>
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%0 Conference Proceedings
%T D3: Dynamic Docid Decoding for Multi-Intent Generative Retrieval
%A Kim, Jaeyoung
%A Lee, Dohyeon
%A Hong, Soona
%A Hwang, Seung-won
%Y Matusevych, Yevgen
%Y Eryiğit, Gülşen
%Y Aletras, Nikolaos
%S Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)
%D 2026
%8 March
%I Association for Computational Linguistics
%C Rabat, Morocco
%@ 979-8-89176-384-5
%F kim-etal-2026-d3
%X Generative Retrieval (GR) maps queries to documents by generating discrete identifiers (DocIDs).However, offline DocID assignment and constrained decoding often prevent GR from capturing query-specific intent, especially when documents express multiple or unseen intents (i.e., intent misalignment).We introduce Dynamic Docid Decoding (D3), an inference-time mechanism that adaptively refines DocIDs through delayed, query-informed identifier expansion.D3 uses (a) verification to detect intent misalignment and (b) dynamic decoding to extend DocIDs with query-aligned tokens, even those absent from the pre-indexed vocabulary, enabling plug-and-play DocID expansion beyond the static vocabulary while adding minimal overhead.Experiments on NQ320k and MS-MARCO show that D3 consistently improves retrieval accuracy, especially on unseen and multi-intent documents, across various GR models, including a +2.4%p nDCG@10 gain on the state-of-the-art model.
%U https://aclanthology.org/2026.eacl-industry.58/
%P 789-800
Markdown (Informal)
[D3: Dynamic Docid Decoding for Multi-Intent Generative Retrieval](https://aclanthology.org/2026.eacl-industry.58/) (Kim et al., EACL 2026)
ACL
- Jaeyoung Kim, Dohyeon Lee, Soona Hong, and Seung-won Hwang. 2026. D3: Dynamic Docid Decoding for Multi-Intent Generative Retrieval. In Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track), pages 789–800, Rabat, Morocco. Association for Computational Linguistics.