Tree of Reviews: A Tree-based Dynamic Iterative Retrieval Framework for Multi-ho

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to reason complex problems step by step, and retrieval-augmentation can effectively alleviate factual errors caused by outdated and unknown knowledge in LLMs.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: What is the accuracy drop on counterfactual multi-hop QA (e.g., Cofca) when using a 128K-context Llama-3 model without retrieval compared to a 4K-context model with 2-step retrieval, controlling for total token budget?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 8.2/10.