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Found 4 papers, 2 papers with code
Just-in-Time Security Patch Detection -- LLM At the Rescue for Data Augmentation
To address this pressing issue, we introduce a novel security patch detection system, LLMDA, which capitalizes on Large Language Models (LLMs) and code-text alignment methodologies for patch review, data enhancement, and feature combination.
Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models
In this paper, we deliver a comprehensive study of PEFT techniques for LLMs under the automated code generation scenario.
On the Usage of Continual Learning for Out-of-Distribution Generalization in Pre-trained Language Models of Code
We demonstrate that the most commonly used fine-tuning technique from prior work is not robust enough to handle the dynamic nature of APIs, leading to the loss of previously acquired knowledge i. e., catastrophic forgetting.
DexBERT: Effective, Task-Agnostic and Fine-grained Representation Learning of Android Bytecode
Central to applying ML to software artifacts (like source or executable code) is converting them into forms suitable for learning.
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