Domain-Specific Dialogue: Fine-Tuning Large Language Models for Enhanced Research Outcomes and Insights - Project Summary

Fine-Tuning Large Language Models for Enhanced Research Outcomes and Insights with Domain-Specific Dialogue

Large Language Models (LLMs) have become pivotal technology, enabling machines to understand and generate human-like replies to questions. Popular pre-trained LLMs have a general understanding of language and capture a wide range of linguistic patterns, but may not perform in specific tasks or domains. Specialized training or fine-tuning is needed to help improve their performance and accuracy. Fine-tuning LLMs allows us to customize the model to a specific domain, enable LLMs to better understand domain-specific terminology, jargon, and context, and could provide enhanced and expedited research outcomes and insights. This use-case will be designed to serve as a foundation for building a versatile documents-to-LLM pipeline, capable of being adapted and reused across various academic disciplines. In addition, education opportunities will be provided to participating students with in-depth knowledge of AI and LLMs, enabling them to develop expertise in designing, and applying these powerful tools to drive innovation and discovery across various academic disciplines.