Skill Neologisms: Towards Skill-based Continual Learning
📰 ArXiv cs.AI
Learn to extend LLM capabilities with skill neologisms, a method to introduce new skills without catastrophic forgetting
Action Steps
- Explore skill neologisms as a method to introduce new skills to LLMs
- Implement soft tokens to integrate new skills without compromising existing ones
- Evaluate the effectiveness of skill neologisms in preventing catastrophic forgetting
- Apply skill neologisms to real-world applications requiring continual learning
- Compare the performance of skill neologisms with other methods such as fine-tuning and context-based approaches
Who Needs to Know This
Researchers and developers working with LLMs can benefit from this approach to expand model capabilities without compromising existing skills. This can be particularly useful in applications where continual learning is crucial.
Key Insight
💡 Skill neologisms can help introduce new skills to LLMs without compromising existing ones, making continual learning more efficient.
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🤖 Extend LLM capabilities with skill neologisms! 🚀 No more catastrophic forgetting. #LLMs #ContinualLearning
Key Takeaways
Learn to extend LLM capabilities with skill neologisms, a method to introduce new skills without catastrophic forgetting
Full Article
Title: Skill Neologisms: Towards Skill-based Continual Learning
Abstract:
arXiv:2605.04970v1 Announce Type: cross Abstract: Modern LLMs show mastery over an ever-growing range of skills, as well as the ability to compose them flexibly. However, extending model capabilities to new skills in a scalable manner is an open-problem: fine-tuning and parameter-efficient variants risk catastrophic forgetting, while context-based approaches have limited expressiveness and are constrained by the model's effective context. We explore skill neologisms--i.e., soft tokens integrated
Abstract:
arXiv:2605.04970v1 Announce Type: cross Abstract: Modern LLMs show mastery over an ever-growing range of skills, as well as the ability to compose them flexibly. However, extending model capabilities to new skills in a scalable manner is an open-problem: fine-tuning and parameter-efficient variants risk catastrophic forgetting, while context-based approaches have limited expressiveness and are constrained by the model's effective context. We explore skill neologisms--i.e., soft tokens integrated
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