Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal¶
Arxiv Link - 2024-05-25 12:17:29
Abstract¶
Large language models (LLMs) suffer from catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training data to retain the model's ability, which may not be feasible in real-world applications. When conducting continual learning based on a publicly-released LLM checkpoint, the availability of the original training data may be non-existent. To address this challenge, we propose a framework called Self-Synthesized Rehearsal (SSR) that uses the LLM to generate synthetic instances for rehearsal. Concretely, we first employ the base LLM for in-context learning to generate synthetic instances. Subsequently, we utilize the latest LLM to refine the instance outputs based on the synthetic inputs, preserving its acquired ability. Finally, we select diverse high-quality synthetic instances for rehearsal in future stages. Experimental results demonstrate that SSR achieves superior or comparable performance compared to conventional rehearsal-based approaches while being more data-efficient. Besides, SSR effectively preserves the generalization capabilities of LLMs in general domains.
Socials¶
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| 🚀 Exciting Breakthrough in AI Research! 🚀 Continual learning with Large Language Models (LLMs) just got a major boost! Conventional methods face challenges with catastrophic forgetting, but a new framework called Self-Synthesized Rehearsal (SSR) is changing the game. 🔍 SSR leverages LLMs to generate synthetic instances for rehearsal, overcoming the need for previous training data. By refining instance outputs based on synthetic inputs, SSR maintains and even enhances the model's abilities. Experimental results show SSR outperforms traditional methods while being more data-efficient. Read more about this groundbreaking research at: http://arxiv.org/abs/2403.01244v2 #AI #LLM #ContinualLearning #TechInnovation #ArtificialIntelligence #NLP |
🚀 New research in continual learning for Large Language Models (LLMs)! Introducing Self-Synthesized Rehearsal (SSR) framework that generates synthetic instances for rehearsal to address catastrophic forgetting. 🧠💡 Superior performance while being data-efficient! Check out the details at: http://arxiv.org/abs/2403.01244v2 #AI #NLP #LLMs #Research #Tech #Innovation 🤖📚 |