Language Model

Large language models (LLMs), despite their advancements, often face difficulties in managing long contexts where information is scattered across the entire text. This phenomenon is referred to as the ‘lost-in-the-middle’ problem, where LLMs struggle to accurately identify and utilize information within such contexts, especially as it becomes distant from the beginning or end. Researchers from…

Large Language Models (LLMs) have made advancements in several sectors such as chatbots and content creation but struggle with extensive computational cost and time required for real-time applications. While various methods have attempted to resolve this, they are often not context-aware and result in inefficient acceptance rates of draft tokens. To address this, researchers from…

In the field of large language models (LLMs), multi-agent debates (MAD) pose a significant challenge due to their high computational costs. They involve multiple agents communicating with one another, all referencing each other's solutions. Despite attempts to improve LLM performance through Chain-of-Thought (CoT) prompting and self-consistency, these methods are still limited by the increased complexity…

Natural evolution has meticulously shaped proteins over more than three billion years. Modern-day research is closely studying these proteins to understand their structures and functions. Large language models are increasingly being employed to interpret the complexities of these protein structures. Such models demonstrate a solid capacity, even without specific training on biological functions, to naturally…

Scientists from Evolutionary Scale PBC, Arc Institute, and the University of California have developed an advanced generative language model for proteins known as ESM3. The protein language model is a sophisticated tool designed to understand and forecast proteins' sequence, structure, and function. It applies the masked language modeling approach to predict masked portions of protein…