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Found 11 papers, 11 papers with code
Improving Medical Reasoning through Retrieval and Self-Reflection with Retrieval-Augmented Large Language Models
To address challenges that still cannot be handled with the encoded knowledge of LLMs, various retrieval-augmented generation (RAG) methods have been developed by searching documents from the knowledge corpus and appending them unconditionally or selectively to the input of LLMs for generation.
Enhancing Label Consistency on Document-level Named Entity Recognition
A notable advantage of NER is its consistency in extracting biomedical entities in a document context.
Ranked #1 on
Named Entity Recognition (NER)
on Gellus
BERN2: an advanced neural biomedical named entity recognition and normalization tool
In biomedical natural language processing, named entity recognition (NER) and named entity normalization (NEN) are key tasks that enable the automatic extraction of biomedical entities (e. g. diseases and drugs) from the ever-growing biomedical literature.
Graph Transformer Networks: Learning Meta-path Graphs to Improve GNNs
Despite the success of GNNs, most existing GNNs are designed to learn node representations on the fixed and homogeneous graphs.
Regularization for Long Named Entity Recognition
Pre-trained language models (PLMs) are used to solve NER tasks and tend to be biased toward dataset patterns such as length statistics, surface form, and skewed class distribution.
Ranked #3 on
Named Entity Recognition (NER)
on WNUT 2017
Transferability of Natural Language Inference to Biomedical Question Answering
We observe that BioBERT trained on the NLI dataset obtains better performance on Yes/No (+5. 59%), Factoid (+0. 53%), List type (+13. 58%) questions compared to performance obtained in a previous challenge (BioASQ 7B Phase B).
Answering Questions on COVID-19 in Real-Time
The recent outbreak of the novel coronavirus is wreaking havoc on the world and researchers are struggling to effectively combat it.
Learning by Semantic Similarity Makes Abstractive Summarization Better
By harnessing pre-trained language models, summarization models had rapid progress recently.
Graph Transformer Networks
In this paper, we propose Graph Transformer Networks (GTNs) that are capable of generating new graph structures, which involve identifying useful connections between unconnected nodes on the original graph, while learning effective node representation on the new graphs in an end-to-end fashion.
Pre-trained Language Model for Biomedical Question Answering
The recent success of question answering systems is largely attributed to pre-trained language models.
HATS: A Hierarchical Graph Attention Network for Stock Movement Prediction
Methods that use relational data for stock market prediction have been recently proposed, but they are still in their infancy.
