Summary of Contrato360 2.0: a Document and Database-driven Question-answer System Using Large Language Models and Agents, by Antony Seabra et al.
Contrato360 2.0: A Document and Database-Driven Question-Answer System using Large Language Models and Agents
by Antony Seabra, Claudio Cavalcante, Joao Nepomuceno, Lucas Lago, Nicolaas Ruberg, Sergio Lifschitz
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- 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 question-and-answer (Q&A) application utilizes a large language model (LLM) to provide accurate and relevant answers by combining information from contract documents (PDFs) and data retrieved from contract management systems (database). The LLM is enhanced through the use of Retrieval-Augmented Generation (RAG), text-to-SQL techniques, and agents that dynamically orchestrate the workflow. This eliminates the need for retraining the language model. Additionally, Prompt Engineering was employed to fine-tune the focus of responses. The results show significant improvement in relevance and accuracy of answers, offering a promising direction for future information systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents an AI-powered Q&A tool that helps with contract management by combining data from contracts and databases. It uses a special language model that’s trained to give accurate answers. To make it even better, the system uses some clever techniques like Retrieval-Augmented Generation and agents that work together to get the best results. The system can be fine-tuned for specific questions, making it very useful for managing contracts. |
Keywords
» Artificial intelligence » Language model » Large language model » Prompt » Rag » Retrieval augmented generation
