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

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

Published on: December 6, 2024

Fine-tuning large language models for chemical text mining.

Wei Zhang^1,2, Qinggong Wang³, Xiangtai Kong^1,2

¹Drug Discovery and Design Center, State Key Laboratory of Drug Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences 555 Zuchongzhi Road Shanghai 201203 China myzheng@simm.ac.cn fuzunyun@simm.ac.cn.

Chemical Science

|July 12, 2024

Summary

This summary is machine-generated.

Fine-tuned large language models (LLMs) significantly improve chemical text mining accuracy across five complex tasks. These advanced LLMs reduce the need for extensive prompt engineering, offering a powerful new tool for automated data acquisition in chemistry.

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Area of Science:

Computational chemistry
Natural Language Processing
Chemical Informatics

Background:

Extracting knowledge from chemical literature is challenging due to complex language.
Automated data acquisition is crucial for both experimental and computational chemists.
Large Language Models (LLMs) show potential for chemical text mining.

Purpose of the Study:

To explore the effectiveness of fine-tuned LLMs on intricate chemical text mining tasks.
To compare fine-tuned LLMs against prompt-engineered models (ChatGPT, GPT-4) and other open-source LLMs.
To assess the performance of LLMs with minimal annotated data.

Main Methods:

Fine-tuning of various LLMs including ChatGPT (GPT-3.5-turbo), GPT-4, Mistral, Llama3, Llama2, T5, and BART.
Evaluation on five chemical text mining tasks: compound entity recognition, reaction role labelling, MOF synthesis extraction, NMR data extraction, and reaction-to-action sequence conversion.
Comparison of fine-tuned models against prompt-engineered models using limited annotated data.

Main Results:

Fine-tuned ChatGPT models achieved high accuracy (69%-95%) across all evaluated tasks.
Fine-tuned LLMs outperformed models using task-adaptive pre-training with larger in-domain datasets.
Fine-tuned Mistral and Llama3 demonstrated competitive performance.
Significant reduction in prompt engineering effort was observed.

Conclusions:

Fine-tuned LLMs are highly effective for chemical knowledge extraction, even with minimal data.
These models offer a versatile, robust, and low-code solution for automated data acquisition.
Leveraging fine-tuned LLMs can revolutionize the field of chemical informatics.