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Found 3 papers, 2 papers with code
Towards Ontology-Enhanced Representation Learning for Large Language Models
Taking advantage of the widespread use of ontologies to organise and harmonize knowledge across several distinct domains, this paper proposes a novel approach to improve an embedding-Large Language Model (embedding-LLM) of interest by infusing the knowledge formalized by a reference ontology: ontological knowledge infusion aims at boosting the ability of the considered LLM to effectively model the knowledge domain described by the infused ontology.
Evaluation of large language model performance on the Biomedical Language Understanding and Reasoning Benchmark
Results Across all tasks, GPT-4 outperformed other LLMs, followed by Flan-T5-XXL and GPT-3. 5-turbo, then Zephyr-7b-Beta and MedLLaMA-13B.
Ranked #1 on
Named Entity Recognition (NER)
on NCBI-disease
(Entity F1 metric)
Autofocus Layer for Semantic Segmentation
We propose the autofocus convolutional layer for semantic segmentation with the objective of enhancing the capabilities of neural networks for multi-scale processing.
Ranked #5 on
Brain Tumor Segmentation
on BRATS-2015
