Authors: Keshav Rangan, Yiqiao Yin

Published on: February 26, 2024

Impact Score: 7.8

Arxiv code: Arxiv:2402.17081

Summary

• What is new: Innovative enhancement of retrieval-augmented generation systems integrating fine-tuned large language models with vector databases, introducing LoRA and QLoRA for refining models and incorporating user feedback directly.
• Why this is important: Existing retrieval-augmented generation systems lack efficiency in model refinement and cannot adapt to user expectations effectively.
• What the research proposes: By integrating fine-tuned large language models with vector databases and using LoRA and QLoRA methodologies for model refinement, alongside direct user feedback integration.
• Results: The enhanced system demonstrates improved performance and applicability in chatbot technologies, with more sophisticated, precise, and user-centric conversational AI systems.

Technical Details

Technological frameworks used: LoRA, QLoRA

Models used: Fine-tuned large language models

Data used: Structured data from vector databases

Potential Impact

Chatbot technologies, conversational AI market, companies in customer service automation

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