Named entity recognition (NER) is the task to identify mentions of rigid designators from text
belonging to predefined semantic types such as person, location, organization etc. NER …

Attention is an increasingly popular mechanism used in a wide range of neural
architectures. The mechanism itself has been realized in a variety of formats. However …

Large" instruction-tuned" language models (ie, finetuned to respond to instructions) have
demonstrated a remarkable ability to generalize zero-shot to new tasks. Nevertheless, they …

Controlling the behavior of language models (LMs) without re-training is a major open
problem in natural language generation. While recent works have demonstrated successes …

Language models have become a key step to achieve state-of-the art results in many
different Natural Language Processing (NLP) tasks. Leveraging the huge amount of …

Conventional task-and modality-specific artificial intelligence (AI) models are inflexible in
real-world deployment and maintenance for biomedicine. At the same time, the growing …

Recent proprietary large language models (LLMs), such as GPT-4, have achieved a
milestone in tackling diverse challenges in the biomedical domain, ranging from multiple …

Neural abstractive summarization models are prone to generate content inconsistent with
the source document, ie unfaithful. Existing automatic metrics do not capture such mistakes …

Natural language understanding comprises a wide range of diverse tasks such as textual
entailment, question answering, semantic similarity assessment, and document …

For most deep learning practitioners, sequence modeling is synonymous with recurrent
networks. Yet recent results indicate that convolutional architectures can outperform …