Summary of Rag For Effective Supply Chain Security Questionnaire Automation, by Zaynab Batool Reza et al.
RAG for Effective Supply Chain Security Questionnaire Automation
by Zaynab Batool Reza, Abdul Rafay Syed, Omer Iqbal, Ethel Mensah, Qian Liu, Maxx Richard Rahman, Wolfgang Maass
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 proposed novel approach uses Natural Language Processing (NLP) and Retrieval-Augmented Generation (RAG) to automate the processing of security-related inquiries through supply chain security questionnaires. The QuestSecure system interprets diverse document formats, integrating large language models (LLMs) with an advanced retrieval system to generate precise responses. Experiments show significant improvements in response accuracy and operational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, researchers developed a system called QuestSecure that helps answer security questions quickly and accurately. It uses special computer programs called natural language processing and retrieval-augmented generation to understand different types of documents and provide good answers. The system is very good at giving the right responses and makes it easier for people in charge of security to get the information they need. |
Keywords
» Artificial intelligence » Natural language processing » Nlp » Rag » Retrieval augmented generation
