What question did this study set out to answer?

The aim is to correct errors in the previously published work regarding generative retrieval methods.

March 18, 2026ACM Transactions on Information SystemsOpen Access

Erratum: Learning Discrete Identifiers and Dense Vectors for Generative Retrieval

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The aim is to correct errors in the previously published work regarding generative retrieval methods.
Identification of inaccuracies in the original article.
Clarification of key concepts related to discrete identifiers and dense vectors.
Correction of data presented in the prior publication.
Corrections enhance the clarity of generative retrieval techniques.
Improved understanding of the application of discrete identifiers.
Clarifications on dense vectors reinforce previous research findings.

Implication

Erratum corrects previous findings on generative retrieval techniques for improved outcomes.

Abstract

This is an erratum for the article “Learning Discrete Identifiers and Dense Vectors for Generative Retrieval” published in ACM Trans. Inf. Syst. 44, 2, Article 42 (December 2025), 24 pages.

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Cite This Study

Xie et al. (2026) studied this question.

synapsesocial.com/papers/69ba430d4e9516ffd37a3eff https://doi.org/10.1145/3795145

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Implication

Erratum corrects previous findings on generative retrieval techniques for improved outcomes.

Abstract

This is an erratum for the article “Learning Discrete Identifiers and Dense Vectors for Generative Retrieval” published in ACM Trans. Inf. Syst. 44, 2, Article 42 (December 2025), 24 pages.

Erratum: Learning Discrete Identifiers and Dense Vectors for Generative Retrieval

Key Points

Implication

Abstract

Discussion

Cite This Study

Erratum: Learning Discrete Identifiers and Dense Vectors for Generative Retrieval

Key Points

Implication

Abstract

Discussion

Cite This Study

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