How does the Tree of Reviews framework compare to standard chain-based retrieval on the MuSiQue multi-hop QA b

Abstract

Abstract: Multi-hop question answering is a knowledge-intensive complex problem. Large Language Models (LLMs) use their Chain of Thoughts (CoT) capability to reason complex problems step by step, and retrieval-augmentation can effectively alleviate factual errors caused by outdated and unknown knowledge in LLMs. Recent works have introduced retrieval-augmentation in the CoT reasoning to solve multi-hop question answering. However, these chain methods have the following problems: 1) Retrieved irrelevant paragraphs may mislead the reasoning; 2) An error in the chain structure may lead to a cascade of erro

Research Question

How does the Tree of Reviews framework compare to standard chain-based retrieval on the MuSiQue multi-hop QA benchmark in terms of accuracy and token efficiency when using a 128K-context Llama-3 model without retrieval?

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Quality Dimensions

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