An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

מושגי ליבה

Catastrophic forgetting is generally observed in large language models ranging from 1 billion to 7 billion parameters during continual instruction tuning, with the severity of forgetting intensifying as the model scale increases. Decoder-only models like BLOOMZ exhibit less forgetting and retain more knowledge compared to encoder-decoder models like mT0. General instruction tuning can help alleviate the forgetting phenomenon in large language models during subsequent fine-tuning processes.

תקציר

This study empirically evaluates the catastrophic forgetting (CF) phenomenon in large language models (LLMs) during continual instruction tuning. The authors analyze the retention of general knowledge in LLMs from the perspectives of domain knowledge, reasoning, and reading comprehension.
The key findings are:

The forgetting problem is generally present in LLMs, with the severity increasing as the model scale grows from 1 billion to 7 billion parameters. Larger models exhibit stronger initial performance but experience more pronounced performance degradation during continual instruction tuning.

Comparing the decoder-only model BLOOMZ with the encoder-decoder model mT0, BLOOMZ exhibits less forgetting and retains more knowledge during continual fine-tuning. This suggests that the decoder-only architecture may be better at preserving information.

The study also observes that LLMs can mitigate language biases, such as gender bias, during continual fine-tuning.

Comparing the initial model LLAMA with its instruction-tuned version ALPACA, the findings indicate that general instruction tuning can help alleviate the forgetting phenomenon in LLMs during subsequent fine-tuning processes.

The authors conclude that exploring more effective methods to mitigate catastrophic forgetting in LLMs during continual fine-tuning is a promising research direction, as it is crucial for the reliable and consistent performance of LLMs in real-world applications.

סטטיסטיקה

The initial performance of BLOOMZ-7.1b on MMLU-Other is 36.18%.
The final performance of BLOOMZ-7.1b on MMLU-Other is 26.35%.
The initial performance of BLOOMZ-1.1b on MMLU-Other is 30.58%.
The final performance of BLOOMZ-1.1b on MMLU-Other is 25.97%.

ציטוטים

"As large language models (LLMs) have demonstrated remarkable performance, it is intriguing to investigate whether CF exists during the continual instruction tuning of LLMs."
"Interestingly, we also observe that LLMs can mitigate language biases, such as gender bias, during continual fine-tuning."
"Furthermore, our findings indicate that ALPACA maintains more knowledge and capacity compared to LLAMA during continual fine-tuning, suggesting that general instruction tuning can help alleviate the forgetting phenomenon in LLMs during subsequent fine-tuning processes."

תובנות מפתח מזוקקות מ:

An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning

by Yun Luo,Zhen... ב- arxiv.org 04-03-2024

https://arxiv.org/pdf/2308.08747.pdf

שאלות מעמיקות

How can the catastrophic forgetting problem in large language models be further mitigated during continual fine-tuning beyond the techniques explored in this study?

To further mitigate the catastrophic forgetting problem in large language models during continual fine-tuning, several additional techniques can be considered:

Regularization Techniques: Implementing regularization methods such as weight decay, dropout, or early stopping can help prevent overfitting and improve the model's ability to retain previously learned knowledge.

Knowledge Distillation: Utilizing knowledge distillation, where a larger pre-trained model transfers its knowledge to a smaller model, can help preserve important information during fine-tuning.

Dynamic Weight Allocation: Implementing dynamic weight allocation mechanisms that prioritize retaining important parameters related to general knowledge while adapting to new task-specific information can help balance the trade-off between old and new knowledge.

Task-Specific Memory Modules: Incorporating task-specific memory modules that store important information from previous tasks and selectively update them during fine-tuning can aid in retaining crucial knowledge.

Multi-Task Learning: Leveraging multi-task learning approaches can enable the model to learn multiple tasks simultaneously, potentially reducing catastrophic forgetting by continuously engaging with a diverse set of tasks.

By combining these techniques with the strategies explored in the study, it is possible to enhance the model's ability to retain general knowledge and mitigate catastrophic forgetting during continual fine-tuning.

What are the potential trade-offs between retaining general knowledge and optimizing for specific task performance in the continual fine-tuning of large language models?

In the continual fine-tuning of large language models, there are several trade-offs between retaining general knowledge and optimizing for specific task performance:

Overfitting vs. Generalization: Focusing too much on retaining general knowledge may lead to overfitting on previous tasks, reducing the model's ability to generalize to new tasks. Balancing the retention of general knowledge with task-specific optimization is crucial for maintaining overall performance.

Task-Specific Adaptation: Prioritizing specific task performance during fine-tuning may result in the model forgetting previously learned general knowledge. Striking a balance between task-specific adaptation and general knowledge retention is essential for achieving optimal performance across tasks.

Resource Allocation: Allocating resources towards retaining general knowledge may limit the model's capacity to adapt to new tasks effectively. Managing resource allocation between retaining knowledge and learning new information is necessary for efficient continual fine-tuning.

Bias and Fairness: Emphasizing specific task performance without considering the retention of general knowledge may lead to biased or unfair outcomes. Ensuring a balance between task optimization and general knowledge retention is crucial for maintaining fairness and reducing bias in the model.

By understanding and addressing these trade-offs, practitioners can optimize the continual fine-tuning process to achieve a balance between retaining general knowledge and optimizing for specific task performance.

How might the insights from this study on catastrophic forgetting in language models inform the development of more general and robust artificial intelligence systems?

The insights from this study on catastrophic forgetting in language models can inform the development of more general and robust artificial intelligence systems in the following ways:

Model Architecture Design: Understanding how different model architectures impact catastrophic forgetting can guide the design of more robust AI systems. By leveraging decoder-only architectures or incorporating memory mechanisms, developers can enhance the model's ability to retain knowledge during continual learning.

Continual Learning Strategies: Insights into the factors influencing catastrophic forgetting, such as model scale and task order, can help refine continual learning strategies. By optimizing the training process and incorporating techniques to mitigate forgetting, AI systems can maintain performance across diverse tasks.

Bias Mitigation: The study's findings on bias mitigation during continual fine-tuning can inform the development of AI systems that are more fair and unbiased. By incorporating techniques to reduce bias while retaining knowledge, developers can create more ethical and inclusive AI models.

Generalization and Adaptation: Understanding the trade-offs between retaining general knowledge and optimizing for specific tasks can aid in developing AI systems that balance adaptability and generalization. By fine-tuning models to preserve essential knowledge while adapting to new tasks, AI systems can achieve better performance and versatility.

By applying the insights from this study, developers can enhance the robustness, adaptability, and fairness of artificial intelligence systems, leading to more effective and reliable AI applications.