Retrieval-Augmented vs. Parametric Models in Large-Scale Code Generation Efficiency

Abstract

Abstract: This report synthesises findings from 14 peer-reviewed papers addressing the following research question: How does the computational efficiency of retrieval-augmented generation (RAG) compare to parametric-only models in large-scale code generation tasks evaluated using the MBPP benchmark. This research presents and compares multiple approaches to automate the generation of literature reviews using several Natural Language Processing (NLP) techniques and retrieval-augmented generation (RAG) with a Large Language Model (LLM). The ever-increasing number of research. 18 claims were extracted from source literature; 6 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 5.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the computational efficiency of retrieval-augmented generation (RAG) compare to parametric-only models in large-scale code generation tasks evaluated using the MBPP benchmark?

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