To cope with real-world dynamics, an intelligent system needs to incrementally acquire,
update, accumulate, and exploit knowledge throughout its lifetime. This ability, known as …

Large language models (LLMs) can potentially democratize access to medical knowledge.
While many efforts have been made to harness and improve LLMs' medical knowledge and …

Existing approaches to lifelong language learning rely on plenty of labeled data for learning
a new task, which is hard to obtain in most real scenarios. Considering that humans can …

Despite differing from the human language processing mechanism in implementation and
algorithms, current language models demonstrate remarkable human-like or surpassing …

Continual learning is essential for real-world deployment when there is a need to quickly
adapt the model to new tasks without forgetting knowledge of old tasks. Existing work on …

The mainstream machine learning paradigms for NLP often work with two underlying
presumptions. First, the target task is predefined and static; a system merely needs to learn …

Lifelong event detection aims to incrementally update a model with new event types and
data while retaining the capability on previously learned old types. One critical challenge is …

To decipher the algorithm underlying the human brain's language representation, previous
work probed brain responses to language input with pre-trained artificial neural network …

Automatic dialogue coherence evaluation has attracted increasing attention and is crucial
for developing promising dialogue systems. However, existing metrics have two major …

In this paper, we introduce SaulLM-medium and SaulLM-large, two large language models
(LLMs) families tailored for the legal sector. These models, which feature architectures of 54 …