MIT's new fine-tuning method lets LLMs learn new skills without losing old ones

MIT researchers unveil a new fine-tuning method that lets enterprises consolidate their "model zoos" into a single, continuously learning agent.

Researchers propose a self-distillation fix for ‘catastrophic forgetting’ in LLMs

LLMs tend to lose prior skills when fine-tuned for new tasks. A new self-distillation approach aims to reduce regression and ...
VentureBeat

Google’s ‘Nested Learning’ paradigm could solve AI's memory and continual learning problem

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 ...
Forbes

Generative AI Becomes Domain Savvy Via In-Context Learning And Advanced Prompt Engineering

Forbes contributors publish independent expert analyses and insights. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant. You would be hard-pressed to say that one or the other is ...

When context is everything, AI models still struggle in the real world: Tencent

Despite rapid advances, today's top AI models are still "brittle" in messy, real-world environments, according to new Tencent research Leading US and Chinese artificial intelligence models are ...
Search Engine Roundtable

Jeff Dean: Combining Google Search With LLM In-Context Learning

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 ...