Authors: Mathav Raj J, Kushala VM, Harikrishna Warrier, Yogesh Gupta

Published on: March 23, 2024

Impact Score: 7.2

Arxiv code: Arxiv:2404.10779

Summary

• What is new: The paper presents a novel approach to fine-tuning Large Language Models (LLMs) using proprietary documents and codes within enterprises, specifically focusing on open-source LLMs like LLaMA and offering detailed guidance on data preparation.
• Why this is important: Enterprises need to tailor LLMs with their proprietary domain knowledge efficiently, facing challenges in optimizing resource use, cost, and time without relying on limited Retrieval Augmented Generation (RAG) methods.
• What the research proposes: Fine-tuning LLaMA with enterprise-specific documents and codes, introducing pre-processing recipes for data in various formats, and providing practical guidelines for efficient model training.
• Results: The fine-tuned models showed improved quality in domain-specific responses, demonstrating the efficacy of the proposed data preparation methods and tuning guidelines.

Technical Details

Technological frameworks used: LLaMA for fine-tuning

Models used: Large Language Models (LLMs)

Data used: Proprietary enterprise documents and code repositories

Potential Impact

This research could impact a wide array of industries reliant on LLMs for handling domain-specific tasks, including tech companies developing AI solutions, legal firms, medical research institutions, and any enterprise aiming to leverage LLMs for proprietary knowledge processing.

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