Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal

Core Concepts

The author proposes the Self-Synthesized Rehearsal (SSR) framework to address catastrophic forgetting in large language models by generating synthetic instances for rehearsal, achieving superior performance compared to conventional methods.

Abstract

The content discusses mitigating catastrophic forgetting in large language models through the SSR framework. SSR generates synthetic instances for rehearsal, outperforming conventional methods and preserving generalization capabilities. Large language models (LLMs) face catastrophic forgetting during continual learning. Conventional rehearsal-based methods rely on previous training data. Proposed SSR framework uses LLM to generate synthetic instances for rehearsal. SSR achieves superior performance and data efficiency compared to conventional approaches. Experiments show SSR preserves generalization capabilities of LLMs in various domains. In continual learning, LLMs update sequentially with instruction data for each stage. Rehearsal-based methods sample training instances from previous stages to expand current training data. SSR generates synthetic instances using base LLM and refines outputs with the latest LLM. Selected high-quality synthetic instances are used for future rehearsals. Regularization-based, architecture-based, and rehearsal-based are main approaches to continual learning. Rehearsal-based methods store a subset of data from previous tasks for future rehearsal. Prior approaches focus on using precedent data but may not be practical in real-world applications. SSR demonstrates superior or comparable performance compared to conventional baselines. Experiments on SuperNI dataset show SSR's effectiveness in mitigating catastrophic forgetting.

Stats

Large language models suffer from catastrophic forgetting during continual learning. Synthetic instances generated by SSR achieve superior or comparable performance compared to conventional methods.

Quotes

Key Insights Distilled From

Mitigating Catastrophic Forgetting in Large Language Models with Self-Synthesized Rehearsal

by Jianheng Hua... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01244.pdf

Deeper Inquiries

How can the SSR framework be adapted for different types of language models

The SSR framework can be adapted for different types of language models by adjusting the specific implementation details to suit the characteristics of the model. For instance, in models with different architectures or training procedures, modifications may be needed in how synthetic instances are generated and refined. Additionally, the selection criteria for rehearsal data may vary based on the requirements of each model. By customizing these aspects according to the particularities of a given language model, SSR can effectively mitigate catastrophic forgetting across a diverse range of models.

What implications does the SSR framework have for other fields beyond natural language processing

The implications of the SSR framework extend beyond natural language processing into various other fields that involve continual learning with large models. In computer vision, for example, where deep learning models face similar challenges with catastrophic forgetting during incremental learning tasks, adapting SSR could help maintain performance on previous tasks while incorporating new information efficiently. Similarly, in reinforcement learning applications where agents need to continually adapt to changing environments and tasks, integrating self-synthesized rehearsal techniques inspired by SSR could enhance agent performance over time without forgetting previously learned behaviors.

How can the concept of self-synthesized rehearsal be applied to other machine learning tasks

The concept of self-synthesized rehearsal introduced in the SSR framework can be applied to other machine learning tasks beyond natural language processing. For instance: In image classification tasks: Synthetic images generated by an image classifier could serve as rehearsal data to prevent forgetting previous classes while adapting to new ones. In speech recognition systems: Self-generated phoneme sequences based on past audio inputs could be used for rehearsal during continual adaptation phases. In recommendation systems: Artificial user interactions or preferences created by collaborative filtering algorithms could act as synthetic instances for maintaining personalized recommendations over time. By leveraging self-synthesized rehearsal strategies tailored to specific machine learning domains and task requirements, continual learning systems across various fields can benefit from improved knowledge retention and adaptation capabilities.