Abstract
Large Language Models (LLMs) are pre-trained on large-scale corpora and excel in numerous general natural language processing (NLP) tasks, such as question answering (QA). Despite their advanced language capabilities, when it comes to domain-specific and knowledge-intensive tasks, LLMs suffer from hallucinations, knowledge cut-offs, and lack of knowledge attributions. Additionally, fine tuning LLMs' intrinsic knowledge to highly specific domains is an expensive and time consuming process. The retrieval-augmented generation (RAG) process has recently emerged as a method capable of optimization of LLM responses, by referencing them to a predetermined ontology. It was shown that using a Knowledge Graph (KG) ontology for RAG improves the QA accuracy, by taking into account relevant sub-graphs that preserve the information in a structured manner. In this paper, we introduce SMART-SLIC, a highly domain-specific LLM framework, that integrates RAG with KG and a vector store (VS) that store factual domain specific information. Importantly, to avoid hallucinations in the KG, we build these highly domain-specific KGs and VSs without the use of LLMs, but via NLP, data mining, and nonnegative tensor factorization with automatic model selection. Pairing our RAG with a domain-specific: (i) KG (containing structured information), and (ii) VS (containing unstructured information) enables the development of domain-specific chat-bots that attribute the source of information, mitigate hallucinations, lessen the need for fine-tuning, and excel in highly domain-specific question answering tasks. We pair SMART-SLIC with chain-of-thought prompting agents. The framework is designed to be generalizable to adapt to any specific or specialized domain. In this paper, we demonstrate the question answering capabilities of our framework on a corpus of scientific publications on malware analysis and anomaly detection.
Abstract (translated)
大规模语言模型（LLMs）在大型语料库上预训练，并在许多通用自然语言处理（NLP）任务中表现出色，如问题回答（QA）。尽管它们具有高级语言能力，但在领域特定和知识密集型任务上，LLMs会受到幻觉、知识截止和知识归因不足的困扰。此外，将LLM的固有知识细分为高度特定的领域是一个耗时且昂贵的过程。最近，检索增强生成（RAG）过程作为一种优化LLM响应的方法而出现，通过将它们与预定义的语义网络参考。研究表明，使用知识图（KG）语义网络对RAG具有更好的QA准确率，通过考虑到相关的子图以保留结构化信息。在本文中，我们介绍了一个高度领域特定的LLM框架SMART-SLIC，该框架将RAG与KG和事实领域特定信息向量存储（VS）集成在一起。重要的是，为了避免知识库中的幻觉，我们通过NLP、数据挖掘和非负张量分解自动选择模型来构建这些高度领域特定的KGs和VS，而不是使用LLM。将我们的RAG与领域特定的： (i) KG（包含结构化信息）和(ii) VS（包含非结构化信息）相结合，可以开发出领域特定的聊天机器人，能够归因信息的来源、减轻幻觉、降低对细调的需求并擅长高度领域特定的问题回答任务。我们将SMART-SLIC与链式思考提示代理商相结合。该框架旨在适用于任何具体或专业领域。本文我们还展示了我们在关于恶意软件分析和检测领域的知识库上问题回答能力的实证研究。
URL
https://arxiv.org/abs/2410.02721
