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Much recent research on information retrieval has focused on how to transferfrom one task (typically with abundant supervised data) to various other taskswhere supervision is limited, with the implicit assumption that it is possibleto generalize from one task to all the rest. However, this overlooks the factthat there are many diverse and unique retrieval tasks, each targetingdifferent search intents, queries, and search domains. In this paper, wesuggest to work on Few-shot Dense Retrieval, a setting where each task comeswith a short description and a few examples. To amplify the power of a fewexamples, we propose Prompt-base Query Generation for Retriever (Promptagator),which leverages large language models (LLM) as a few-shot query generator, andcreates task-specific retrievers based on the generated data. Powered by LLM'sgeneralization ability, Promptagator makes it possible to create task-specificend-to-end retrievers solely based on a few examples {without} using NaturalQuestions or MS MARCO to train %question generators or dual encoders.Surprisingly, LLM prompting with no more than 8 examples allows dual encodersto outperform heavily engineered models trained on MS MARCO like ColBERT v2 bymore than 1.2 nDCG on average on 11 retrieval sets. Further trainingstandard-size re-rankers using the same generated data yields another 5.0 pointnDCG improvement. Our studies determine that query generation can be far moreeffective than previously observed, especially when a small amount oftask-specific knowledge is given.

Promptagator: Few-shot Dense Retrieval From 8 Examples