AI for Science: Automating Domain Specific Tasks with Large Language Models

Recent advancements in Large Language Models (LLMs) have transformed various fields by demonstrating remarkable capabilities in processing and generating human-like text. This project aims to explore the development of an open-source framework that leverages LLMs to enhance discovery across specialized domains.

The proposed framework will enable LLMs to analyze and interpret complex datasets, automate routine tasks, and uncover novel insights. A key focus will be on equipping LLMs with domain-specific expertise, particularly in areas where specialized tools – such as ANDES – are not widely integrated with LLM-based solutions. By bridging this gap, the framework will empower researchers and professionals to harness LLMs as intelligent assistants capable of navigating and utilizing niche computational tools effectively.

AI for Science: Automating Domain Specific Tasks with Large Language Models

• Topics: Large Language Models AI for Science
• Skills: Python, Experience with LLMs, Prompt Engineering, Fine-Tuning, LLM Frameworks
• Difficulty: Medium-Difficult
• Size: Large (350 hours)
• Mentor: [Daniel Wong]Daniel Wong, [Luanzheng “Lenny” Guo]Luanzheng "Lenny" Guo

Project Tasks and Milestones

• Designing an extensible framework that facilitates the integration of LLMs with specialized software and datasets.
• Developing methodologies for fine-tuning LLMs to act as domain experts.
• Implementing strategies for improving tool interoperability, allowing LLMs to interact seamlessly with less commonly used but critical analytical platforms.