ACID: Abstractive, Content-Based IDs for Document Retrieval with Language Models

• URL: http://arxiv.org/abs/2311.08593v1
• Date: Tue, 14 Nov 2023 23:28:36 GMT
• Title: ACID: Abstractive, Content-Based IDs for Document Retrieval with Language Models
• Authors: Haoxin Li, Phillip Keung, Daniel Cheng, Jungo Kasai, Noah A. Smith
• Abstract summary: We introduce ACID, in which each document's ID is composed of abstractive keyphrases generated by a large language model. We show that using ACID improves top-10 and top-20 accuracy by 15.6% and 14.4% (relative) Our results demonstrate the effectiveness of human-readable, natural-language IDs in generative retrieval with LMs.
• Score: 69.86170930261841
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Generative retrieval (Wang et al., 2022; Tay et al., 2022) is a new approach for end-to-end document retrieval that directly generates document identifiers given an input query. Techniques for designing effective, high-quality document IDs remain largely unexplored. We introduce ACID, in which each document's ID is composed of abstractive keyphrases generated by a large language model, rather than an integer ID sequence as done in past work. We compare our method with the current state-of-the-art technique for ID generation, which produces IDs through hierarchical clustering of document embeddings. We also examine simpler methods to generate natural-language document IDs, including the naive approach of using the first k words of each document as its ID or words with high BM25 scores in that document. We show that using ACID improves top-10 and top-20 accuracy by 15.6% and 14.4% (relative) respectively versus the state-of-the-art baseline on the MSMARCO 100k retrieval task, and 4.4% and 4.0% respectively on the Natural Questions 100k retrieval task. Our results demonstrate the effectiveness of human-readable, natural-language IDs in generative retrieval with LMs. The code for reproducing our results and the keyword-augmented datasets will be released on formal publication.

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