Language Model

Large Language Models (LLMs) have surpassed previous generations of language models on various tasks, sometimes even equating or surpassing human performance. However, it's challenging to evaluate their true capabilities due to potential contamination in testing datasets or a lack of datasets that accurately assess their abilities. Most studies assessing LLMs have focused primarily on the English…

Neural network models are dominant in the areas of natural language processing and computer vision. However, the initialization and learning rates of these models often depend on heuristic methods, which can lead to inconsistencies across different studies and model sizes. The µ-Parameterization (µP) seeks to address this issue by proposing scaling rules for model parameters…

Researchers from Mila, McGill University, ServiceNow Research, and Facebook CIFAR AI Chair have developed a method called LLM2Vec to transform pre-trained decoder-only Large Language Models (LLMs) into text encoders. Modern NLP tasks highly depend on text embedding models that translate text's semantic meaning into vector representations. Historically, pre-trained bidirectional encoding models such as BERT and…

Computational linguistics has seen significant advancements in recent years, particularly in the development of Multilingual Large Language Models (MLLMs). These are capable of processing a multitude of languages simultaneously, which is critical in an increasingly globalized world that requires effective interlingual communication. MLLMs address the challenge of efficiently processing and generating text across various languages,…

In recent years, there has been increasing attention paid to the development of Small Language Models (SLMs) as a more efficient and cost-effective alternative to Large Language Models (LLMs), which are resource-heavy and present operational challenges. In this context, researchers from the Department of Computer Science and Technology at Tsinghua University and Modelbest Inc. have…

Researchers from Meta/FAIR Labs and Mohamed bin Zayed University of AI have carried out a detailed exploration into the scaling laws for large language models (LLMs). These laws delineate the relationship between factors such as a model's size, the time it takes to train, and its overall performance. While it’s commonly held that larger models…

The field of Natural Language Processing (NLP) has witnessed a radical transformation following the advent of Large Language Models (LLMs). However, the prevalent Transformer architecture used in these models suffers from quadratic complexity issues. While techniques such as sparse attention have been developed to lower this complexity, a new generation of models is making headway…

Causal learning plays a pivotal role in the effective operation of artificial intelligence (AI), helping improve AI models' ability to rationalize decisions, adapt to new data, and visualize hypothetical scenarios. However, the evaluation of large language models' (LLM) proficiency in processing causality, such as GPT-3 and its variants, remains a challenge due to the need…