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Updated: Sep 18, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
Published on: December 6, 2024
Rethinking chemical research in the age of large language models
Robert MacKnight1, Daniil A Boiko1, Jose Emilio Regio2
1Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.
Abstract:
Large language models (LLMs) offer opportunities for advancing chemical research, including planning, optimization, data analysis, automation and knowledge management. Deploying LLMs in active environments, where they interact with tools and data, can greatly enhance their capabilities. However, challenges remain in evaluating their performance and addressing ethical issues such as reproducibility, data privacy and bias. Here we discuss ongoing and potential integrations of LLMs in chemical research, highlighting existing challenges to guide the effective use of LLMs as active scientific partners.
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