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A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
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Grammar-constrained decoding for structured information extraction with fine-tuned generative models applied to

David M Schmidt1, Philipp Cimiano1

  • 1Center for Cognitive Interaction Technology (CITEC), Technical Faculty, Bielefeld University, Bielefeld, Germany.

Frontiers in Artificial Intelligence
|January 22, 2025
PubMed
Summary

Grammar-constrained decoding significantly improves information extraction in low-resource settings, enhancing structured data generation from clinical trial abstracts. Pointer generators, however, decreased performance, highlighting the importance of guided decoding for accuracy.

Keywords:
PICOclinical trialsdeep learningevidence-based medicinegenerative large language modelsgrammar-constrained decodingstructured information extraction

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Area of Science:

  • Natural Language Processing
  • Information Extraction
  • Machine Learning

Background:

  • Standard encoder-decoder models struggle to guarantee semantic and syntactic constraints in information extraction (IE).
  • Constrained decoding approaches, utilizing context-free grammars, offer a way to enforce output structure.
  • The effectiveness of domain-specific grammars in guiding IE systems for domain data model compliance remains an open question.

Purpose of the Study:

  • To experimentally investigate the impact of grammar-constrained decoding and pointer generators on domain-specific IE performance.
  • To evaluate if these techniques improve information extraction results for fine-tuned encoder-decoder models (Longformer, Flan-T5).
  • To assess performance in low-resource settings with limited training data (hundreds of examples).

Main Methods:

  • Framed the task as slot filling for structured representations from clinical trial abstracts, using the C-TrO ontology.
  • Employed Longformer and Flan-T5 encoder-decoder models, comparing performance with and without grammar-constrained decoding and pointer generators.
  • Utilized a dataset of 211 annotated clinical trial abstracts for type 2 diabetes and glaucoma.

Main Results:

  • Grammar-constrained decoding demonstrated a positive impact, increasing F1 scores by 0.351 (to 0.413) for type 2 diabetes and 0.425 (to 0.47) for glaucoma.
  • Pointer generators negatively impacted results, decreasing F1 scores by 0.15 (to 0.263) for type 2 diabetes and 0.198 (to 0.272) for glaucoma.
  • Best performance gains from grammar-constrained decoding were observed in low-resource settings.

Conclusions:

  • Encoder-decoder models for structure prediction in low-resource IE tasks benefit significantly from grammar-constrained decoding.
  • Pointer generator models showed decreased performance, with outcomes dependent on the base model and attention aggregation.
  • Further research is needed to structurally investigate how large language model size influences the benefits of grammar-constrained decoding.