Vulnerability detection is essential for software quality assurance. In
recent years, deep learning models (especially large language models) have
shown promise in vulnerability detection. In this work, we propose a novel
LLM-based vulnerability detection technique Vul-RAG, which leverages
knowledge-level retrieval-augmented generation (RAG) framework to detect
vulnerability for the given code in three phases. First, Vul-RAG constructs a
vulnerability knowledge base by extracting multi-dimension knowledge via LLMs
from existing CVE instances; second, for a given code snippet, Vul-RAG}
retrieves the relevant vulnerability knowledge from the constructed knowledge
base based on functional semantics; third, Vul-RAG leverages LLMs to check the
vulnerability of the given code snippet by reasoning the presence of
vulnerability causes and fixing solutions of the retrieved vulnerability
knowledge. Our evaluation of Vul-RAG on our constructed benchmark PairVul shows
that Vul-RAG substantially outperforms all baselines by 12.96\%/110\% relative
improvement in accuracy/pairwise-accuracy. In addition, our user study shows
that the vulnerability knowledge generated by Vul-RAG can serve as high-quality
explanations which can improve the manual detection accuracy from 0.60 to 0.77.

使用 LL 元模型构建脆弱性知识库，并利用知识级检索增强生成（RAG）框架检测给定代码的脆弱性，取得显著的改进。

Vul-RAG: 基于知识级 RAG 的 LLM 漏洞检测增强

Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level  RAG

In recent years, large language models (LLM) have emerged as powerful tools
for diverse natural language processing tasks. However, their potential for
recommender systems under the generative recommendation paradigm remains
relatively unexplored. This paper presents an innovative approach to
recommendation systems using large language models (LLMs) based on text data.
In this paper, we present a novel LLM for generative recommendation (GenRec)
that utilized the expressive power of LLM to directly generate the target item
to recommend, rather than calculating ranking score for each candidate item one
by one as in traditional discriminative recommendation. GenRec uses LLM's
understanding ability to interpret context, learn user preferences, and
generate relevant recommendation. Our proposed approach leverages the vast
knowledge encoded in large language models to accomplish recommendation tasks.
We first we formulate specialized prompts to enhance the ability of LLM to
comprehend recommendation tasks. Subsequently, we use these prompts to
fine-tune the LLaMA backbone LLM on a dataset of user-item interactions,
represented by textual data, to capture user preferences and item
characteristics. Our research underscores the potential of LLM-based generative
recommendation in revolutionizing the domain of recommendation systems and
offers a foundational framework for future explorations in this field. We
conduct extensive experiments on benchmark datasets, and the experiments shows
that our GenRec has significant better results on large dataset.

使用大型语言模型 (LLM) 为基础的生成式推荐 (GenRec) 方法，通过解释上下文、学习用户偏好和生成相关推荐，充分利用大型语言模型编码的丰富知识，为推荐系统带来革命性的潜力和未来发展方向。