A critic-based verification mechanism where a second “critic” prompt reviews and verifies extracted relations is investigated, demonstrating that this approach is highly effective, reducing false positives by 58.5% and achieving 74.1% precision with top models, competitive with supervised methods.
A benchmark-guided, scalable framework for automated medical terminology standardization that accepts heterogeneous short medical expressions without manual input pre-processing and automatically performs text refinement, semantic retrieval and terminology mapping to standardized concepts and vocabulary codes is establ...
Anshul Verma, Abhijay, Manan Vangani et al.· bioRxiv· 0 citations
This paper introduces a fully graph-based pipeline where the classifier never directly processes the input text, and reveals that graphs and text are complementary: combining both modalities achieves 92.1% accuracy on SNLI.
Younes Boufouss, Luc Pommeret, Thomas Gerald et al.· 0 citations
The rapid expansion of biomedical literature requires automated methods for accurate and efficient information extraction. This study addresses relation classification: given a pair of annotated biomedical entities in a research article title and abstract, assigning the relation that holds between them from a pre-defin...
Jannat, Charlie Dil, Tom Arodz et al.· Frontiers in Research Metric...· 0 citations
This work presents a scalable, reproducible framework for evaluating, optimizing, and interpreting LLMs for biomedical knowledge extraction, with a focus on gene–gene regulatory relation prediction, pathway component recognition, multimodal pathway figure understanding, and automated prompt optimization.
