Augmenting Large Language Models via Vector Embeddings to Improve Domain-specific Responsiveness.

Area of Science:

• Artificial Intelligence
• Natural Language Processing
• Bioinformatics

Background:

• Large language models (LLMs) are trained on static data, limiting their use in dynamic fields.
• Current LLMs struggle with rapidly changing information, proprietary data, and sensitive datasets.
• Domain-specific LLM adaptation is crucial for fields like scientific research.

Purpose of the Study:

• To outline methods for augmenting general-purpose LLMs (foundation models) with domain-specific, up-to-date scientific information.
• To enable the use of current, peer-reviewed scientific manuscripts for LLM enhancement.
• To facilitate the development of LLM systems for specialized domains.

Main Methods:

• An embeddings-based approach is used to incorporate new textual data into existing LLMs.
• Open-source tools like Llama-Index and publicly available models like Llama-2 are utilized.
• Methods for evaluating model performance after augmentation are discussed.

Main Results:

• The described methods allow for the integration of current scientific literature into LLMs.
• The approach enhances LLMs with domain-specific knowledge without requiring complete retraining.
• Performance evaluation methods are provided for augmented LLM systems.

Conclusions:

• This approach enables the rapid development of specialized LLM systems.
• It overcomes limitations of static training corpora for LLMs in dynamic knowledge domains.
• The method promotes transparency, user privacy, and replicability in LLM development.