Large Language Models are Built-in Autoregressive Search Engines
Noah Ziems Wenhao Yu Zhihan Zhang Meng Jiang
Document retrieval is a key stage of standard Web search engines. Existing
dual-encoder dense retrievers obtain representations for questions and
documents independently, allowing for only shallow interactions between them.
To overcome this limitation, recent autoregressive search engines replace the
dual-encoder architecture by directly generating identifiers for relevant
documents in the candidate pool. However, the training cost of such
autoregressive search engines rises sharply as the number of candidate
documents increases. In this paper, we find that large language models (LLMs)
can follow human instructions to directly generate URLs for document retrieval.
  Surprisingly, when providing a few {Query-URL} pairs as in-context
demonstrations, LLMs can generate Web URLs where nearly 90\% of the
corresponding documents contain correct answers to open-domain questions. In
this way, LLMs can be thought of as built-in search engines, since they have
not been explicitly trained to map questions to document identifiers.
Experiments demonstrate that our method can consistently achieve better
retrieval performance than existing retrieval approaches by a significant
margin on three open-domain question answering benchmarks, under both zero and
few-shot settings. The code for this work can be found at
\url{https://github.com/Ziems/llm-url}.