Large Language Model

Large Language Models (LLMs) such as GPT-4 are highly proficient in text generation tasks including summarization and question answering. However, a common problem is their tendency to generate “hallucinations,” which refers to the production of factually incorrect or contextually irrelevant content. This problem becomes critical when it occurs despite the LLMs being given correct facts,…

Large language models (LLMs) such as GPT-4 have shown impressive capabilities in generating text for summarization and question answering tasks. But these models often “hallucinate,” or produce content that is either contextually irrelevant or factually incorrect. This is particularly concerning in applications where accuracy is crucial, such as document-based question answering and summarization, and where…

Transformer-based Large Language Models (LLMs) like ChatGPT and LLaMA are highly effective in tasks requiring specialized knowledge and complex reasoning. However, their massive computational and storage requirements present significant challenges in wider applications. One solution to this problem is quantization, a method that converts 32-bit parameters into smaller bit sizes, which greatly improves storage efficiency…

A new study attempts to address the limitations associated with next-token prediction methods in artificial intelligence (AI), which currently hinder the technology's ability to mimic human intelligence, specifically in the area of advance planning and reasoning. Featuring in a multitude of language models today, these methods are increasingly shown to be deficient when it comes…

Artificial intelligence research often examines whether next-token prediction—the convention for AI language models—can replicate some aspects of human intelligence such as planning and reasoning. However, despite its extensive use, this method may have native limitations when it comes to tasks necessitating foresight and decision-making. This is important because overcoming this could allow the development of…

FlashAttention-3, the newest addition to the FlashAttention series, was created to address the fundamental issues related to Transformer architectures' attention layer. This is particularly important to the performance of large language models (LLMs) and applications that need long-context processing. Historically, the FlashAttention series, which includes FlashAttention and FlashAttention-2, has reshaped how attention mechanisms function on GPUs…