What is the accuracy drop on the HotpotQA multi-hop dataset when using a 128K-context Llama-3 model without re

Abstract

Abstract: Prompting-based large language models (LLMs) are surprisingly powerful at generating natural language reasoning steps or Chains-of-Thoughts (CoT) for multi-step question answering (QA). They struggle, however, when the necessary knowledge is either unavailable to the LLM or not up-to-date within its parameters. While using the question to retrieve relevant text from an external knowledge source helps LLMs, we observe that this one-step retrieve-and-read approach is insufficient for multi-step QA. Here, what to retrieve depends on what has already been derived, which in turn may depend on what

Research Question

What is the accuracy drop on the HotpotQA multi-hop dataset when using a 128K-context Llama-3 model without retrieval versus a 4K-context model with 2-step retrieval, controlling for total token budget?

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Provenance