Retrieval-Augmentation Context Effects on Llama-3-8B-128K Accuracy in Jamendo-MT-QA

Abstract

Abstract: This report synthesises findings from 15 peer-reviewed papers addressing the following research question: What is the impact of varying retrieval-augmentation contexts (e.g., different music metadata sources, retrieval depths) on Llama-3-8B-128K's response accuracy for fact-based versus interpretive. Recent work on music question answering (Music-QA) has primarily focused on single-track understanding, where models answer questions about an individual audio clip using its tags, captions, or metadata. However, listeners often describe music in comparative terms, and existing. 11 claims were extracted from source literature; 3 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 6.0/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

What is the impact of varying retrieval-augmentation contexts (e.g., different music metadata sources, retrieval depths) on Llama-3-8B-128K's response accuracy for fact-based versus interpretive questions in Jamendo-MT-QA, measured by both G-EVAL and GPT-4 scoring?

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