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Retrieval-augmented in-context learning has emerged as a powerful approachfor addressing knowledge-intensive tasks using frozen language models (LM) andretrieval models (RM). Existing work has combined these in simple"retrieve-then-read" pipelines in which the RM retrieves passages that areinserted into the LM prompt. To begin to fully realize the potential of frozenLMs and RMs, we propose Demonstrate-Search-Predict (DSP), a framework thatrelies on passing natural language texts in sophisticated pipelines between anLM and an RM. DSP can express high-level programs that bootstrap pipeline-awaredemonstrations, search for relevant passages, and generate groundedpredictions, systematically breaking down problems into small transformationsthat the LM and RM can handle more reliably. We have written novel DSP programsfor answering questions in open-domain, multi-hop, and conversational settings,establishing in early evaluations new state-of-the-art in-context learningresults and delivering 37-120%, 8-39%, and 80-290% relative gains against thevanilla LM (GPT-3.5), a standard retrieve-then-read pipeline, and acontemporaneous self-ask pipeline, respectively. We release DSP athttps://github.com/stanfordnlp/dsp

Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP