Answering questions related to the legal domain is a complex task, primarily due to the
intricate nature and diverse range of legal document systems. Providing an accurate answer …

Transformer-based language models (TLMs) have widely been recognized to be a cutting-
edge technology for the successful development of deep-learning-based solutions to …

Readers of academic research papers often read with the goal of answering specific
questions. Question Answering systems that can answer those questions can make …

In the fast-evolving domain of artificial intelligence, large language models (LLMs) such as
GPT-3 and GPT-4 are revolutionizing the landscapes of finance, healthcare, and law …

The VNHSGE (VietNamese High School Graduation Examination) dataset, developed
exclusively for evaluating large language models (LLMs), is introduced in this article. The …

Large models, encompassing large language and diffusion models, have shown
exceptional promise in approximating human-level intelligence, garnering significant …

Despite large successes of recent language models on diverse tasks, they suffer from
severe performance degeneration in low-resource settings with limited training data …

We describe a Question Answering (QA) dataset that contains complex questions with
conditional answers, ie the answers are only applicable when certain conditions apply. We …

Large Language Models (LLMs) have demonstrated impressive zero shot performance on a
wide range of NLP tasks, demonstrating the ability to reason and apply commonsense. A …

Decomposable tasks are complex and comprise of a hierarchy of sub-tasks. Spoken intent
prediction, for example, combines automatic speech recognition and natural language …