Fine-Tuning Llama3 on Big-Vul Dataset Enhances FeedbackEval Benchmark Performance

Abstract

Abstract: This report synthesises findings from 13 peer-reviewed papers addressing the following research question: How does fine-tuning Llama3 with the Big-Vul dataset's vulnerability classification annotations impact its performance on the FeedbackEval benchmark compared to the base model. Detecting toxic content using language models is crucial yet challenging. While substantial progress has been made in English, toxicity detection in French remains underdeveloped, primarily due to the lack of culturally relevant, human-annotated, large-scale datasets. 13 claims were extracted from source literature; 1 was independently verified against retrieved documents. An automated multi-reviewer quality assessment produced a score of 4.4/10. This report is a machine-generated literature synthesis and does not constitute original research.

Research Question

How does fine-tuning Llama3 with the Big-Vul dataset's vulnerability classification annotations impact its performance on the FeedbackEval benchmark compared to the base model?

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