Semantic Retrieval Augmentation Effects on LLM Pass@k in Multi-File Code Generation

Abstract

Abstract: This report synthesises findings from 11 peer-reviewed papers addressing the following research question: How does semantic retrieval augmentation impact pass@k scores for LLMs on multi-file code generation benchmarks compared to standard context window extension. Large Language Models (LLMs) showcase impressive capabilities but encounter challenges like hallucination, outdated knowledge, and non-transparent, untraceable reasoning processes. Retrieval-Augmented Generation (RAG) has emerged as a promising solution by incorporating. 5 claims were extracted from source literature; 5 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 8.5/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does semantic retrieval augmentation impact pass@k scores for LLMs on multi-file code generation benchmarks compared to standard context window extension?

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