In this work, we propose a simple method that applies a large language model (LLM) to large-scale retrieval in zero-shot scenarios. Our method, the Language language model as Retriever (LameR), is built upon no other neural models but an LLM, while breaking brute-force combinations of retrievers with LLMs and lifting the performance of zero-shot retrieval to be very competitive on benchmark datasets. Essentially, we propose to augment a query with its potential answers by prompting LLMs with a composition of the query and the query's in-domain candidates. The candidates, regardless of correct or wrong, are obtained by a vanilla retrieval procedure on the target collection. As a part of the prompts, they are likely to help LLM generate more precise answers by pattern imitation or candidate summarization. Even if all the candidates are wrong, the prompts at least make LLM aware of in-collection patterns and genres. Moreover, due to the low performance of a self-supervised retriever, the LLM-based query augmentation becomes less effective as the retriever bottlenecks the whole pipeline. Therefore, we propose to leverage a non-parametric lexicon-based method (e.g., BM25) as the retrieval module to capture query-document overlap in a literal fashion. As such, LameR makes the retrieval procedure transparent to the LLM, thus circumventing the performance bottleneck.

翻译：本文提出一种简单方法，将大语言模型（LLM）应用于零样本场景下的大规模检索任务。我们的方法——语言模型作为检索器（LameR）——仅基于大语言模型构建，无需其他神经模型，同时摒弃了检索器与LLM的暴力组合方式，使零样本检索性能在基准数据集上达到极具竞争力的水平。具体而言，我们通过将查询与其领域内候选答案组合作为提示输入LLM，从而对查询进行增强。这些候选答案（无论正确与否）均通过目标数据集上的普通检索流程获取。作为提示的一部分，它们能通过模式模仿或候选总结帮助LLM生成更精确的答案。即便所有候选答案均错误，提示至少能让LLM掌握集合内模式与体裁特征。此外，由于自监督检索器性能低下，基于LLM的查询增强会因检索器成为整个流程的瓶颈而效果减弱。因此，我们采用非参数化的词法方法（如BM25）作为检索模块，以字面方式捕获查询-文档重叠。通过这种方式，LameR使检索过程对LLM透明，从而规避了性能瓶颈。