Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Legal judgment prediction (LJP) applies Natural Language Processing (NLP) techniques to
predict judgment results based on fact descriptions automatically. The present work …

Transformers are slow and memory-hungry on long sequences, since the time and memory
complexity of self-attention are quadratic in sequence length. Approximate attention …

AI is undergoing a paradigm shift with the rise of models (eg, BERT, DALL-E, GPT-3) that are
trained on broad data at scale and are adaptable to a wide range of downstream tasks. We …

Language Models (LMs) have been shown to leak information about training data through
sentence-level membership inference and reconstruction attacks. Understanding the risk of …

The advent of large language models (LLMs) and their adoption by the legal community has
given rise to the question: what types of legal reasoning can LLMs perform? To enable …

BERT has achieved impressive performance in several NLP tasks. However, there has been
limited investigation on its adaptation guidelines in specialised domains. Here we focus on …

Automated legal text classification is a prominent research topic in the legal field. It lays the
foundation for building an intelligent legal system. Current literature focuses on international …

Legal Artificial Intelligence (LegalAI) focuses on applying the technology of artificial
intelligence, especially natural language processing, to benefit tasks in the legal domain. In …

Legal artificial intelligence (LegalAI) aims to benefit legal systems with the technology of
artificial intelligence, especially natural language processing (NLP). Recently, inspired by …