Google's Nested Learning aims to stop LLMs from catastrophic forgetting
Google Research has introduced "nested learning," a new way to design AI models that aims to mitigate or even avoid "catastrophic forgetting" and support continuous learning.<br /> The article Google's Nested Learning aims to stop LLMs from catastrophic forgetting appeared first on THE DECODER. [...]
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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 trai [...]
Researchers find that retraining only small parts of AI models can cut costs and prevent forgetting
Enterprises often find that when they fine-tune models, one effective approach to making a large language model (LLM) fit for purpose and grounded in data is to have the model lose some of its abiliti [...]
MIT's new fine-tuning method lets LLMs learn new skills without losing old ones
When enterprises fine-tune LLMs for new tasks, they risk breaking everything the models already know. This forces companies to maintain separate models for every skill.Researchers at MIT, the Improbab [...]
Self-improving language models are becoming reality with MIT's updated SEAL technique
Researchers at the Massachusetts Institute of Technology (MIT) are gaining renewed attention for developing and open sourcing a technique that allows large language models (LLMs) — like those underp [...]
Fine-tuning forgets. RAG leaks context. Hypernetworks build the model your agent needs on demand.
Enterprise teams keep watching the same thing happen. An AI agent demos beautifully, goes to production, and stalls: it runs for a short stretch, then needs a human to top up its context and check its [...]
MIT's MeMo lets teams swap in a better LLM without retraining — and performance jumps 26%
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]
MeMo's memory model lets teams upgrade their LLM without retraining it — and performance jumps 26%
Enabling LLMs to acquire new knowledge after training remains a major hurdle for enterprise AI — current solutions are either too expensive, too slow, or constrained by context window limits.MeMo, a [...]