Summary of Domain-specific Retrieval-augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization, by Ryan C. Barron et al.
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization
by Ryan C. Barron, Ves Grantcharov, Selma Wanna, Maksim E. Eren, Manish Bhattarai, Nicholas Solovyev, George Tompkins, Charles Nicholas, Kim Ø. Rasmussen, Cynthia Matuszek, Boian S. Alexandrov
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Information Retrieval (cs.IR); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The research introduces SMART-SLIC, a novel large language model (LLM) framework that leverages retrieval-augmented generation (RAG) with a knowledge graph (KG) ontology and vector store (VS) to develop domain-specific chat-bots. The proposed approach integrates RAG with KG and VS, which are built without relying on LLMs using NLP, data mining, and non-negative tensor factorization with automatic model selection. This framework enables the development of domain-specific chat-bots that attribute information sources, mitigate hallucinations, and excel in highly domain-specific question answering tasks. The researchers demonstrate the capabilities of their framework on a corpus of scientific publications on malware analysis and anomaly detection. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SMART-SLIC project creates a special kind of computer program called a large language model that can understand and answer questions. These programs are very good at understanding general information, but they struggle when it comes to specific areas like medicine or science. To solve this problem, the researchers developed a new way to make these programs better by using something called retrieval-augmented generation (RAG) with a special kind of database that organizes information in a structured way. This allows the program to provide answers and give credit where it’s due. The researchers tested their approach on a set of scientific papers about malware analysis and anomaly detection, and it worked well. |
Keywords
» Artificial intelligence » Anomaly detection » Knowledge graph » Large language model » Nlp » Question answering » Rag » Retrieval augmented generation
