Adversarial Contrastive Learning and Few-Shot Prompting for Robust Code Generation

Abstract

Abstract: This report synthesises findings from 12 peer-reviewed papers addressing the following research question: Does adversarial contrastive learning with few-shot prompting improve robustness to adversarial examples in code generation tasks evaluated on HumanEval, measured by pass@1 and pass@k metrics. Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and generation is acquired by training. 10 claims were extracted from source literature; 9 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

Does adversarial contrastive learning with few-shot prompting improve robustness to adversarial examples in code generation tasks evaluated on HumanEval, measured by pass@1 and pass@k metrics?

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