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Found 8 papers, 1 papers with code
Large Language Models, scientific knowledge and factuality: A systematic analysis in antibiotic discovery
The results show that while LLMs are currently not fit for purpose to be used as biomedical factual knowledge bases, there is a promising emerging property in the direction of factuality as the models become domain specialised, scale-up in size and level of human feedback.
On the Visualisation of Argumentation Graphs to Support Text Interpretation
Therefore, AGs were considered to deliver a more critical approach to argument interpretation, especially with unfamiliar topics.
A systematic review of biologically-informed deep learning models for cancer: fundamental trends for encoding and interpreting oncology data
We discuss the recent evolutionary arch of DL models in the direction of integrating prior biological relational and network knowledge to support better generalisation (e. g. pathways or Protein-Protein-Interaction networks) and interpretability.
Metareview-informed Explainable Cytokine Storm Detection during CAR-T cell Therapy
Cytokine release syndrome (CRS), also known as cytokine storm, is one of the most consequential adverse effects of chimeric antigen receptor therapies that have shown promising results in cancer treatment.
Assessing the communication gap between AI models and healthcare professionals: explainability, utility and trust in AI-driven clinical decision-making
This paper contributes with a pragmatic evaluation framework for explainable Machine Learning (ML) models for clinical decision support.
Transformers and the representation of biomedical background knowledge
Specialised transformers-based models (such as BioBERT and BioMegatron) are adapted for the biomedical domain based on publicly available biomedical corpora.
Architectures of Meaning, A Systematic Corpus Analysis of NLP Systems
This paper proposes a novel statistical corpus analysis framework targeted towards the interpretation of Natural Language Processing (NLP) architectural patterns at scale.
What is SemEval evaluating? A Systematic Analysis of Evaluation Campaigns in NLP
SemEval is the primary venue in the NLP community for the proposal of new challenges and for the systematic empirical evaluation of NLP systems.
