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University of Illinois at Urbana-Champaign

Language models as semantic indexers

Abstract

dc:description

Semantic identifier (ID) is an important concept in information retrieval that aims to preserve the semantics of objects such as documents and items inside their IDs. Previous studies typically adopt a two-stage pipeline to learn semantic IDs by first procuring embeddings using off-the-shelf text encoders and then deriving IDs based on the embeddings. However, each step introduces potential information loss, and there is usually an inherent mismatch between the distribution of embeddings within the latent space produced by text encoders and the anticipated distribution required for semantic indexing. Nevertheless, it is non-trivial to design a method that can learn the document’s semantic representations and its hierarchical structure simultaneously, given that semantic IDs are discrete and sequentially structured, and the semantic supervision is deficient. In this paper, we introduce LMIndexer, a self-supervised framework to learn semantic IDs with a generative language model. We tackle the challenge of sequential discrete ID by introducing a semantic indexer capable of generating neural sequential discrete representations with progressive training and contrastive learning. In response to the semantic supervision deficiency, we propose to train the model with a self-supervised document reconstruction objective. The learned semantic indexer can facilitate various downstream tasks, such as recommendation and retrieval. We conduct experiments on three tasks including recommendation, product search, and document retrieval on five datasets from various domains, where LMIndexer outperforms competitive baselines significantly and consistently.

Degree

thesis:*
Name thesis:degree_name
M.S.
Level thesis:degree_level
Thesis
Discipline thesis:degree_discipline
Computer Science
Grantor
University of Illinois at Urbana-Champaign
Year dc:date
2023

Author and committee

dc:creator, dc:contributor.*
Author dc:creator
  • Jin, Bowen
Contributors dc:contributor
  • Han, Jiawei

Subjects

dc:subject × 2

Rights

dc:rights
Statement dc:rights
  • Copyright 2023 Bowen Jin
Language dc:language
en, eng

Identifiers

dc:identifier.*
Handle dc:identifier
https://hdl.handle.net/2142/122118

Chain of custody

source
Harvested from
University of Illinois - Urbana-Champaign
Base URL
www.ideals.illinois.edu/oai-pmh
Last updated
2026-07-22
Source record
OAI-PMH GetRecord
citation

Jin, Bowen. Language models as semantic indexers. Thesis thesis, University of Illinois at Urbana-Champaign, 2023. https://hdl.handle.net/2142/122118