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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…