Language Models As Semantic Indexers
Bowen Jin Hansi Zeng Guoyin Wang Xiusi Chen Tianxin Wei Ruirui Li Zhengyang Wang Zheng Li Yang Li Hanqing Lu Suhang Wang Jiawei Han Xianfeng Tang
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.