Language Model

In the field of machine learning and artificial language modeling, Large Language Models (LLMs) are often used to analyze or interpret large chunks of data. Such models have the capability to support very long context windows; however, this approach is not without its challenges. Standard attention mechanisms, used to allocate computational resources, often suffer from…

Artificial Intelligence (AI) has revolutionized numerous industries, from customer service to content generation, by deploying large language models (LLMs) that can supply accurate and useful replies to human prompts. However, these models tend to favor longer responses, exhibiting an inherent length bias that complicates model evaluation. To balance response length with quality, researchers have developed Length-Instruction…

Large Language Models (LLMs) have significantly impacted industries from translation to sentiment analysis. However, their practical use is hampered by computational demands, particularly with long prompts due to the quadratic complexity of the attention mechanism. Addressing this issue, researchers from Microsoft Corporation and the University of Surrey have developed MInference, a method to accelerate long-sequence…

Natural language processing (NLP), a field within artificial intelligence (AI), aims at aiding machines to decipher and establish human language. It includes tasks such as translation, sentiment analysis, and text summarization. The progress in this field has led to the creation of 'Large Language Models’ (LLMs), capable of handling massive quantities of text. This progress…

Natural language processing (NLP), a subfield of Artificial Intelligence (AI), is designed to allow machines to understand and mirror human language. It oversees a variety of tasks like language translation, sentiment analysis, and text summarization. The advent of large language models (LLMs), capable of processing great amounts of data, has significantly advanced these tasks, opening…