Summary of Rag4itops: a Supervised Fine-tunable and Comprehensive Rag Framework For It Operations and Maintenance, by Tianyang Zhang et al.
RAG4ITOps: A Supervised Fine-Tunable and Comprehensive RAG Framework for IT Operations and Maintenance
by Tianyang Zhang, Zhuoxuan Jiang, Shengguang Bai, Tianrui Zhang, Lin Lin, Yang Liu, Jiawei Ren
First submitted to arxiv on: 21 Oct 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 This paper proposes an efficient and supervised fine-tunable framework called RAG4ITOps, which combines Retrieval Augmented Generation (RAG) with contrastive learning to build domain-specific Question Answering (QA) systems for IT operations and maintenance. The framework consists of two stages: models fine-tuning & data vectorization, and online QA system process. In the first stage, the authors leverage a contrastive learning method with two negative sampling strategies to fine-tune the embedding model and design instruction templates to fine-tune Large Language Models (LLMs) using a Retrieval Augmented Fine-Tuning method. The framework is evaluated on enterprise-exclusive corpora from cloud computing, achieving superior results compared to counterparts on two kinds of QA tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make computers better at answering questions about specific areas like cloud computing. It creates a way to improve computer models by fine-tuning them with special data and making sure they can answer questions correctly. This is important for big companies that need to keep their information safe and private. The authors test this method on real-world data and show it works better than other methods. |
Keywords
» Artificial intelligence » Embedding » Fine tuning » Question answering » Rag » Retrieval augmented generation » Supervised » Vectorization
