In artificial intelligence, one common challenge is ensuring that language models can process information quickly and efficiently. Imagine you’re trying to use a language model to generate text or answer questions on your device, but it’s taking too long to respond. This delay can be frustrating and impractical, especially in real-time applications like chatbots or… →

In the ever-evolving field of machine learning, developing models that predict and explain their reasoning is becoming increasingly crucial. As these models grow in complexity, they often become less transparent, resembling “black boxes” where the decision-making process is obscured. This opacity is problematic, particularly in sectors like healthcare and finance, where understanding the basis of… →

Long-context large language models (LLMs) have garnered attention, with extended training windows enabling processing of extensive context. However, recent studies highlight a challenge: these LLMs struggle to utilize middle information effectively, termed the lost-in-the-middle challenge. While the LLM can comprehend the information at the beginning and end of the long context, it often overlooks the… →

In-context learning (ICL) in large language models (LLMs) utilizes input-output examples to adapt to new tasks without altering the underlying model architecture. This method has transformed how models handle various tasks by learning from direct examples provided during inference. The problem at hand is the limitation of a few-shot ICL in handling intricate tasks. These… →