  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.
