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

Jiarui Yao1,2, Zinaida Perova3, Tushar Mandloi3

  • 1Computational Health Informatics Program, Boston Children's Hospital, 401 Park Drive, Boston, MA, United States, 1 7813545014.

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Summary
This summary is machine-generated.

Soft prompting significantly boosts performance of open large language models (LLMs) for extracting patient-derived cancer model (PDCM) entities from scientific texts, rivaling proprietary models.

Keywords:
in-context learninginformation extractionknowledge extractionlarge language modelspatient-derived cancer modelsprompt tuningsoft prompting

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Area of Science:

  • Biomedical Informatics
  • Artificial Intelligence in Oncology
  • Computational Biology

Background:

  • Patient-derived cancer models (PDCMs) are crucial for cancer research and preclinical studies.
  • The volume of PDCM-related publications has surged, necessitating efficient knowledge extraction.
  • Large language models (LLMs) offer advanced capabilities for processing scientific literature at scale.

Purpose of the Study:

  • To investigate LLM-based systems for automated extraction of PDCM-related entities.
  • To compare direct prompting and soft prompting techniques for entity extraction.

Main Methods:

  • Explored direct prompting (manual prompt design) and soft prompting (trainable continuous vectors).
  • Evaluated both approaches across proprietary (GPT4-o) and open (LLaMA3) LLMs.
  • Utilized a manually annotated dataset of 100 PDCM abstracts with 15 entity types.

Main Results:

  • GPT4-o with direct prompting achieved F1-scores of 50.48 (exact match) and 71.36 (overlapping match).
  • LLaMA3 soft prompting significantly improved performance over direct prompting (exact match: 7.06 to 46.68; overlapping match: 12.0 to 71.80).
  • LLaMA3 soft prompting slightly outperformed GPT4-o direct prompting in the overlapping match setting.

Conclusions:

  • Soft prompting enhances the performance of smaller open LLMs for PDCM entity extraction.
  • Training soft prompts on open models can yield performance comparable to proprietary LLMs.
  • This approach facilitates scalable knowledge discovery in PDCM research.