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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Improving biomedical entity linking for complex entity mentions with LLM-based text simplification.

Florian Borchert1, Ignacio Llorca1, Matthieu-P Schapranow1

  • 1Hasso Plattner Institute for Digital Engineering, University of Potsdam, Prof.-Dr.-Helmert-Straße 2-3, Potsdam 14482, Germany.

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|July 27, 2024
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Summary
This summary is machine-generated.

Simplifying complex medical terms using generative large language models improves entity linking accuracy in biomedical text. This approach enhances recall and top-1 accuracy for identifying medical concepts.

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Area of Science:

  • Biomedical Informatics
  • Natural Language Processing

Background:

  • Biomedical research and healthcare generate vast amounts of free-text data.
  • Entity linking is crucial for accessing this information via natural language processing (NLP).
  • Complex, multi-token entity mentions pose challenges for accurate normalization and concept mapping.

Purpose of the Study:

  • To develop a method for preprocessing complex entity mentions in biomedical text.
  • To improve candidate generation for entity linking using text simplification.
  • To evaluate the approach on the BioCreative VIII SympTEMIST shared task.

Main Methods:

  • Utilizing generative large language models for text simplification of complex entity mentions.
  • Applying few-shot prompting with a Generative Pre-trained Transformer (GPT) model.
  • Integrating the simplified mentions into an entity linking pipeline for candidate generation and reranking.

Main Results:

  • Text simplification resulted in more easily normalizable mention spans.
  • Recall during candidate generation improved by 2.9 percentage points.
  • Top-1 accuracy was enhanced by translating recall improvements into reranking, achieving 63.6% on the test set.

Conclusions:

  • Generative large language models can effectively simplify complex biomedical entities for improved NLP.
  • The proposed text simplification method enhances entity linking performance, particularly recall and accuracy.
  • The approach is integrated into the open-source xMEN toolkit for broader application.