Tree of Reviews vs. Chain-Based Retrieval: Latency-Accuracy Trade-offs in Multi-Hop QA for Llama-3-8B-128K

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: What is the trade-off between retrieval latency and answer accuracy when scaling the number of hops in Tree of Reviews vs. chain-based retrieval for Llama-3-8B-128K on the HotPotQA and MuSiQue. Retrieval-augmented generation (RAG) enhances large language models (LLMs) with external knowledge to answer questions more accurately. However, research on evaluating RAG systems-particularly the retriever component-remains limited, as most existing work focuses on. 12 claims were extracted from source literature; 3 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the trade-off between retrieval latency and answer accuracy when scaling the number of hops in Tree of Reviews vs. chain-based retrieval for Llama-3-8B-128K on the HotPotQA and MuSiQue benchmarks?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Quality Tier

Descriptive public triage only; this tier does not alter current publication or DOI behavior.

Quality Dimensions

Automated triage signals derived from public fields; not human peer review or independent validation.

Correction Record

Public corrections are additive records. Current status does not claim the synthesis is error-free.

Provenance