The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily available or affordable.

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 ...

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

Researchers at Google have developed a new AI paradigm aimed at solving one of the biggest limitations in today’s large language models: their inability to learn or update their knowledge after ...

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 ...

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, ...

In April 2023, a few weeks after the launch of GPT-4, the Internet went wild for two new software projects with the audacious names BabyAGI and AutoGPT. “Over the past week, developers around the ...

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 ...

Dwarkesh Patel interviewed Jeff Dean and Noam Shazeer of Google and one topic he asked about what would it be like to merge or combine Google Search with in-context learning. It resulted in a ...