Foundation language models obtain the instruction-following ability thro...
Mathematical reasoning is a challenging task for large language models
(...
Fine-tuning large pre-trained language models on various downstream task...
Previous studies have revealed that vanilla pre-trained language models
...
Recent studies have demonstrated the potential of cross-lingual
transfer...
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignm...
Vision-and-language multi-modal pretraining and fine-tuning have shown g...
Molecular dynamic simulations are important in computational physics,
ch...
Cross-domain NER is a challenging task to address the low-resource probl...
Diffusion model, a new generative modelling paradigm, has achieved great...
Reasoning, as an essential ability for complex problem-solving, can prov...
Language models with the Transformers structure have shown great perform...
Few-shot Named Entity Recognition (NER) aims to identify named entities ...
Prompt learning approaches have made waves in natural language processin...
With the dramatically increased number of parameters in language models,...
Prompt-based fine-tuning has boosted the performance of Pre-trained Lang...
Multimodal named entity recognition and relation extraction (MNER and MR...
Multimodal Knowledge Graphs (MKGs), which organize visual-text factual
k...
Pre-trained language models have contributed significantly to relation
e...
Pretrained language models can be effectively stimulated by textual prom...
Automatic ICD coding is defined as assigning disease codes to electronic...
Previous knowledge graph embedding approaches usually map entities to
re...
We present a new open-source and extensible knowledge extraction toolkit...
Natural language generation from structured data mainly focuses on
surfa...
Nested entities are observed in many domains due to their compositionali...
Event argument extraction (EAE) is an important task for information
ext...
Recent pretrained language models extend from millions to billions of
pa...
Most existing NER methods rely on extensive labeled data for model train...
Large-scale pre-trained language models have contributed significantly t...
In recent years, research on adversarial attacks has become a hot spot.
...
Artificial Intelligence (AI), along with the recent progress in biomedic...
Document-level relation extraction aims to extract relations among multi...
Pretrained language models have shown success in many natural language
p...
In this paper, we reformulate the relation extraction task as mask langu...
Recent studies in deep learning have shown significant progress in named...
Recently, a variety of probing tasks are proposed to discover linguistic...
Recent neural-based relation extraction approaches, though achieving
pro...
Question Answering (QA) is a benchmark Natural Language Processing (NLP)...
Named entity recognition (NER) is a well-studied task in natural languag...
Learning to control the structure of sentences is a challenging problem ...
Clinical trials provide essential guidance for practicing Evidence-Based...
Triple extraction is an essential task in information extraction for nat...
Different functional areas of the human brain play different roles in br...
In this paper, we study a novel task that learns to compose music from
n...
As a new classification platform, deep learning has recently received
in...
The electroencephalography classifier is the most important component of...
Electroencephalography (EEG) has become the most significant input signa...
In this paper, we present a novel approach to machine reading comprehens...
We present a simple yet effective approach for linking entities in queri...
Automatic question generation aims to generate questions from a text pas...