Fine-Tuning Hyperparameters and Cross-Language Generalization in Code LLMs on Big-Vul

Abstract

Abstract: This report synthesises findings from 16 peer-reviewed papers addressing the following research question: How does the choice of fine-tuning hyperparameters impact the cross-language generalization performance of Llama3, Codestral, and Deepseek R1 on Big-Vul, as measured by F1-score gaps between seen and. Anomaly detection is a widely explored domain in machine learning. Many models are proposed in the literature, and compared through different metrics measured on various datasets. 21 claims were extracted from source literature; 0 were independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 2.2/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does the choice of fine-tuning hyperparameters impact the cross-language generalization performance of Llama3, Codestral, and Deepseek R1 on Big-Vul, as measured by F1-score gaps between seen and unseen programming languages?

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