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.

通过生成式检索方法和使用大型语言模型生成抽象关键短语来构成文件的 ID，与现有的文档 ID 生成方法相比，我们的 ACID 方法在 MSMARCO 100k 检索任务和自然问题 100k 检索任务上分别使得前 10 和前 20 的准确率相对提高了 15.6％和 14.4％以及 4.4％和 4.0％。