Language Models As Semantic Indexers
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866913389074186240 |
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| author | Jin, Bowen Zeng, Hansi Wang, Guoyin Chen, Xiusi Wei, Tianxin Li, Ruirui Wang, Zhengyang Li, Zheng Li, Yang Lu, Hanqing Wang, Suhang Han, Jiawei Tang, Xianfeng |
| author_facet | Jin, Bowen Zeng, Hansi Wang, Guoyin Chen, Xiusi Wei, Tianxin Li, Ruirui Wang, Zhengyang Li, Zheng Li, Yang Lu, Hanqing Wang, Suhang Han, Jiawei Tang, Xianfeng |
| contents | 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. 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. We show the high quality of the learned IDs and demonstrate their effectiveness on three tasks including recommendation, product search, and document retrieval on five datasets from various domains. Code is available at https://github.com/PeterGriffinJin/LMIndexer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_07815 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Language Models As Semantic Indexers Jin, Bowen Zeng, Hansi Wang, Guoyin Chen, Xiusi Wei, Tianxin Li, Ruirui Wang, Zhengyang Li, Zheng Li, Yang Lu, Hanqing Wang, Suhang Han, Jiawei Tang, Xianfeng Information Retrieval Computation and Language Machine Learning 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. 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. We show the high quality of the learned IDs and demonstrate their effectiveness on three tasks including recommendation, product search, and document retrieval on five datasets from various domains. Code is available at https://github.com/PeterGriffinJin/LMIndexer. |
| title | Language Models As Semantic Indexers |
| topic | Information Retrieval Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2310.07815 |