How does fine-tuning Llama3, Codestral, and Deepseek R1 on the full Big-Vul dataset impact their vulnerability

Abstract

Abstract: With the advent of the modern pre-trained Transformers, the text preprocessing has started to be neglected and not specificly addressed in recent NLP literature. However, both from a linguistic and from a computer science point of view, we believe that even when using modern Transformers, text preprocessing can significantly impact on the performance of a classification model. We want to investigate and compare, through this study, how preprocessing impacts on the Text Classification (TC) performance of modern and traditional classification models. We report and discuss the preprocessing techn

Research Question

How does fine-tuning Llama3, Codestral, and Deepseek R1 on the full Big-Vul dataset impact their vulnerability classification accuracy and consistency across different programming languages?

Verification Level

Descriptive public verification status only; aggregate claim counts are public, but individual claim records are not exposed here.

Truth-Engine Gate Verdict

This record has not completed Gate 2 of the verification pipeline (a type-checked Lean4 proof for mathematical claims, or a sealed-sandbox reproduction for empirical claims). It is a literature synthesis only. VERIFIED requires an attached reproducible artifact (Lean4 proof source, or repro script and results) before this status can be set; it is not derived from review score or claim count.

Quality Tier

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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