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Prompting-based large language models (LLMs) are surprisingly powerful atgenerating natural language reasoning steps or Chains-of-Thoughts (CoT) formulti-step question answering (QA). They struggle, however, when the necessaryknowledge is either unavailable to the LLM or not up-to-date within itsparameters. While using the question to retrieve relevant text from an externalknowledge source helps LLMs, we observe that this one-step retrieve-and-readapproach is insufficient for multi-step QA. Here, \textit{what to retrieve}depends on \textit{what has already been derived}, which in turn may depend on\textit{what was previously retrieved}. To address this, we propose IRCoT, anew approach for multi-step QA that interleaves retrieval with steps(sentences) in a CoT, guiding the retrieval with CoT and in turn usingretrieved results to improve CoT. Using IRCoT with GPT3 substantially improvesretrieval (up to 21 points) as well as downstream QA (up to 15 points) on fourdatasets: HotpotQA, 2WikiMultihopQA, MuSiQue, and IIRC. We observe similarsubstantial gains in out-of-distribution (OOD) settings as well as with muchsmaller models such as Flan-T5-large without additional training. IRCoT reducesmodel hallucination, resulting in factually more accurate CoT reasoning. Code,data, and prompts are available at \url{https://github.com/stonybrooknlp/ircot}

Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions