Deep learning models have become fundamental tools in drug design. In particular, large language models trained on biochemical sequences learn feature vectors that guide drug discovery through virtual ...

Large language models (LLMs) increasingly mimic human cognition in various language-based tasks. However, their capacity for metacognition—particularly in predicting memory performance—remains ...

Two popular approaches for customizing large language models (LLMs) for downstream tasks are fine-tuning and in-context learning (ICL). In a recent study, researchers at Google DeepMind and Stanford ...

What if the next generation of AI systems could not only understand context but also act on it in real time? Imagine a world where large language models (LLMs) seamlessly interact with external tools, ...

Large language models have moved out of the research lab and into engineers’ daily workflow. LLMs serve as reasoning engines ...

Large language models represent text using tokens, each of which is a few characters. Short words are represented by a single token (like “the” or “it”), whereas larger words may be represented by ...

Modern large language models (LLMs) might write beautiful sonnets and elegant code, but they lack even a rudimentary ability to learn from experience. Researchers at Massachusetts Institute of ...

The arrival of AI systems called large language models (LLMs), like OpenAI’s ChatGPT chatbot, has been heralded as the start of a new technological era. And they may indeed have significant impacts on ...

Why AI agents stall in production: fine-tuning forgets, RAG leaks context. Hypernetworks generate a task-specific model from ...