Bash-Commenter applies CPT, SFT, and Syntax-Aware Preference Optimization (SAPO) via AST atomic operations to LLaMA-3.1-8B, reporting higher BLEU-4/METEOR/ROUGE-L scores than baselines on single-line and multi-line Bash comment generation tasks.
Smart-llama: Two-stage post-training of large language models for smart contract vulnerability detection and explanation,
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2026 3verdicts
UNVERDICTED 3representative citing papers
BashCoder-R1 applies CPT, L-CoT SFT, and R-GRPO to reach higher syntax, robustness, and functionality rates than baselines on the new BashBench benchmark of 952 tasks.
A decoupled four-stage LLM pipeline with rsLoRA, distillation, and CoVe aggregation outperforms larger models on smart contract vulnerability detection and explanation using only 0.6B-4B parameter models.
citing papers explorer
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Bash-Commenter: Leveraging Syntax-Aware Preference Optimization to Reinforce Large Language Model for Bash Code Comment Generation
Bash-Commenter applies CPT, SFT, and Syntax-Aware Preference Optimization (SAPO) via AST atomic operations to LLaMA-3.1-8B, reporting higher BLEU-4/METEOR/ROUGE-L scores than baselines on single-line and multi-line Bash comment generation tasks.
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BashCoder-R1: Towards Robust and Explainable Bash Code Generation with Robustness-Aware Group Relative Policy Optimization
BashCoder-R1 applies CPT, L-CoT SFT, and R-GRPO to reach higher syntax, robustness, and functionality rates than baselines on the new BashBench benchmark of 952 tasks.