Staff

Mixture-of-experts (MoE) architectures, designed for better scaling of model sizes and more efficient inference and training, present a challenge to optimize due to their non-differentiable, discrete nature. Traditional MoEs use a router network which directs input data to expert modules, a process that is complex and can lead to inefficiencies and under-specialization of expert modules.…

Large Language Models (LLMs) play a crucial role in computational linguistics. However, their enormous size and the massive computational demands they require make deploying them very challenging. To faciliate simpler computations and boost model performance, a process of "quantization" is used, which simplifies the data involved. Traditional quantization techniques convert high-precision numbers into lower-precision integers,…

The recent development of large language models (LLMs), which can generate high-quality content across various domains, has revolutionized the field of natural language creation. These models are fundamentally of two types: those with open-source model weights and data sources, and those for which all model-related information, including training data, data sampling ratios, logs, checkpoints, and…

In the world of medical technology, the use of large language models (LLMs) is becoming instrumental, largely due to their ability to analyse and discern copious amounts of medical text, providing insight that would typically require extensive human expertise. The evolution of such technology could lead to substantial reductions in healthcare costs and broaden access…

Information extraction (IE) is a crucial aspect of artificial intelligence, which involves transforming unstructured text into structured and actionable data. Traditional large language models (LLMs), while having high capacities, often struggle to properly comprehend and perform detailed specific directives necessary for effective IE. This problem is particularly evident in closed IE tasks that require adherence…

Structured commonsense reasoning in natural language processing (NLP) is a vital research area focusing on enabling machines to understand and reason about everyday scenarios like humans. It involves translating natural language into interlinked concepts that mirror human logical reasoning. However, it's consistently challenging to automate and accurately model commonsense reasoning. Traditional methodologies often require robust mechanisms…