Summary of Fine-tuning and Utilization Methods Of Domain-specific Llms, by Cheonsu Jeong
Fine-tuning and Utilization Methods of Domain-specific LLMs
by Cheonsu Jeong
First submitted to arxiv on: 1 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 investigates approaches to fine-tuning Large Language Models (LLMs) for specific domains, such as finance. The study highlights trends and methods for pre-training LLMs, focusing on the financial sector. It explores dataset selection, preprocessing, model choice, and considerations crucial for LLM fine-tuning in finance. The researchers detail the construction of domain-specific vocabularies and consider regulatory compliance. They also outline a procedure for generating domain-specific LLMs in finance and demonstrate their potential applications in areas like stock price prediction, sentiment analysis, document processing, research, information extraction, and customer service enhancement. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how to make Large Language Models (LLMs) better suited for specific tasks, like helping with financial work. It talks about the importance of choosing the right data, models, and techniques when working with LLMs in finance. The researchers also discuss ways to create special vocabularies for financial topics and follow rules for security and compliance. They show how LLMs can be used to predict stock prices, understand news articles, process documents, find information, and even help customers. |
Keywords
» Artificial intelligence » Fine tuning
