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

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Extracting Knowledge from Scientific Texts on Patient-Derived Cancer Models Using Large Language Models: Algorithm

Jiarui Yao¹, Zinaida Perova², Tushar Mandloi²

¹Computational Health Informatics Program, Boston Children's Hospital, Harvard Medical School, 401 Park Drive, Boston, MA 02115, USA.

Biorxiv : the Preprint Server for Biology

|February 20, 2025

Summary

This summary is machine-generated.

Large Language Models (LLMs) can automatically extract patient-derived cancer model (PDCM) information from scientific texts. Soft prompting enhances smaller LLMs to perform comparably to larger proprietary models in this task.

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Creating Matched In vivo/In vitro Patient-Derived Model Pairs of PDX and PDX-Derived Organoids for Cancer Pharmacology Research

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Published on: May 5, 2021

Area of Science:

Biomedical Informatics
Artificial Intelligence in Oncology

Background:

Patient-derived cancer models (PDCMs) are crucial for cancer research and preclinical studies, with a notable increase in publications.
Artificial Intelligence (AI), especially Large Language Models (LLMs), offers potential for large-scale knowledge extraction from scientific literature.

Purpose of the Study:

To investigate the efficacy of LLM-based systems for automated extraction of PDCM-related entities from scientific texts.
To compare direct prompting and soft prompting methods using state-of-the-art LLMs.

Main Methods:

Evaluated direct prompting (manual prompts with instructions, definitions, examples) and soft prompting (automatically trained continuous vector prompts).
Utilized proprietary GPT4-o and open LLaMA3 family models for experiments.

Main Results:

GPT4-o with direct prompts achieved competitive results.
Soft prompting significantly enhanced smaller open LLMs, yielding performance comparable to proprietary models.
Demonstrated LLMs' potential for domain-specific text extraction.

Conclusions:

Soft prompting is an effective technique for improving the performance of smaller LLMs in PDCM entity extraction.
Tailoring LLM approaches to specific tasks and model characteristics is crucial for optimal results in scientific text mining.