Tree of Reviews Iterative Retrieval Depth versus Fixed-Depth CoT for Multi-Hop QA Accuracy on HotpotQA

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to reason complex problems step by step, and retrieval-augmentation can effectively alleviate factual errors caused by outdated and unknown knowledge in LLMs.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 goal: How does the depth of iterative retrieval in Tree of Reviews impact the accuracy of multi-hop QA compared to fixed-depth CoT retrieval-augmented models on HotpotQA benchmark?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.0/10.