An Empirical Study of Catastrophic Forgetting in Large Language Models During Continual Fine-tuning
Study on catastrophic forgetting in LLMs during continual fine-tuning; BLOOMZ shows less forgetting than mT0.
Key Findings
Methodology
The study uses BLOOMZ and mT0 models to evaluate knowledge forgetting during continual instruction fine-tuning. Five instruction tasks are used, and evaluations are conducted on domain knowledge, reasoning, and reading comprehension.
Key Results
- Result 1: Forgetting increases with model scale from 1b to 7b parameters. BLOOMZ-7.1b shows a 26.75% forgetting rate in reading comprehension.
- Result 2: BLOOMZ exhibits less forgetting compared to mT0 at the same scale, indicating the decoder-only architecture retains information better.
- Result 3: Language biases, such as gender bias, are mitigated during continual fine-tuning.
Significance
The study highlights the issue of catastrophic forgetting in large language models, particularly the increased forgetting with larger models. The findings are significant for practical applications, indicating the need to consider knowledge retention during task-specific fine-tuning.
Technical Contribution
Introduces a method to evaluate catastrophic forgetting in LLMs during continual instruction fine-tuning, providing empirical data on the impact of model architecture and scale on forgetting.
Novelty
First systematic evaluation of knowledge forgetting in generative LLMs during continual instruction fine-tuning, highlighting differences across architectures and scales.
Limitations
- Limitation 1: The study is limited to models with 1b to 7b parameters, excluding larger models.
- Limitation 2: The selection of experimental tasks is limited and may not fully reflect all application scenarios.
- Limitation 3: The impact of different task orders on forgetting is not deeply explored.
Future Work
Future research could expand to larger models, explore more task combinations, and investigate optimizing fine-tuning strategies to further mitigate forgetting.
AI Executive Summary
Large language models excel at handling multiple tasks but may forget previously learned knowledge during continual fine-tuning. This study explores this phenomenon, particularly in models ranging from 1b to 7b parameters, finding that forgetting increases with model scale. By comparing BLOOMZ and mT0 models, the study finds that decoder-only architectures better retain information. Additionally, continual fine-tuning can reduce language biases, such as gender bias.
The study uses five instruction tasks for fine-tuning, including text simplification, empathetic dialogue generation, inquisitive question generation, explanation generation, and headline generation. Experimental results show that BLOOMZ exhibits less forgetting, especially in reading comprehension and reasoning tasks. However, as more tasks are introduced, knowledge forgetting remains significant.
The significance of this study lies in revealing potential challenges in the practical application of large language models, especially in scenarios requiring multiple fine-tunings. Future research can further explore optimizing fine-tuning strategies to mitigate forgetting and expand to larger models and more task combinations.
Deep Analysis
Background
Large language models have made significant progress in natural language processing, capable of handling various tasks. However, as tasks increase, models may forget previously learned knowledge, known as catastrophic forgetting. In practical applications, models need to be continuously updated to adapt to new tasks, necessitating research on mitigating forgetting.
Core Problem
Catastrophic forgetting refers to the phenomenon where models forget old tasks while learning new ones. For large language models, this issue is particularly important as they are often used in multi-task scenarios, where forgetting can lead to performance degradation.
Innovation
This study systematically evaluates knowledge forgetting in large language models during continual instruction fine-tuning for the first time. By comparing different architectures and scales, the study reveals the advantages of decoder-only architectures in retaining information and introduces a new evaluation method.
Methodology
- �� Select BLOOMZ and mT0 models for comparison
- �� Use five instruction tasks for fine-tuning
- �� Evaluate from domain knowledge, reasoning, and reading comprehension
- �� Use open-source evaluation framework lm-evaluation-harness for performance measurement
Experiments
The experimental design includes selecting BLOOMZ and mT0 models, fine-tuning them across 1.1b to 7.1b parameter scales. Five instruction tasks, including text simplification and empathetic dialogue generation, are used to evaluate model performance across different tasks.
Results
Experimental results show that forgetting increases with model scale. BLOOMZ exhibits less forgetting at the same scale, especially in reading comprehension and reasoning tasks.
Applications
The study's findings are significant for application scenarios requiring multiple fine-tunings of large language models, such as intelligent customer service systems and personalized recommendation systems.
Limitations & Outlook
The study is limited to models with 1b to 7b parameters, excluding larger models. Additionally, the selection of experimental tasks is limited and may not fully reflect all application scenarios.
Plain Language Accessible to non-experts
Imagine you're learning new dance moves, and each time you learn a new move, you might forget the previous ones. This is the challenge faced by large language models during continual fine-tuning. The study finds that as models get larger, they are more prone to forgetting past knowledge. However, some model architectures can better retain information, just like some people are better at remembering dance moves.
ELI14 Explained like you're 14
Imagine playing a game where you keep leveling up. Each time you level up, you learn new skills but sometimes forget the old ones. That's the problem with large language models during continual fine-tuning. The study finds that as models get bigger, they forget old skills more easily. But some models, like game characters, are better at remembering skills. This is important for developing smarter AI assistants!
Glossary
Catastrophic Forgetting
Refers to the phenomenon where models forget old tasks while learning new ones.
During continual fine-tuning, models may forget previously learned knowledge.
Continual Learning
A machine learning paradigm where models learn a sequence of tasks incrementally.
The study evaluates the performance of large language models in continual learning.
Decoder Architecture
A neural network architecture typically used for generative tasks.
The study finds that decoder architectures better retain information.
Domain Knowledge
Specialized knowledge within a specific field.
Evaluates the extent of forgetting in different domain knowledge areas.
Bias Mitigation
Reducing biases in models related to gender, race, etc.
The study finds that continual fine-tuning can reduce language biases.
Open Questions Unanswered questions from this research
- 1 How to effectively mitigate catastrophic forgetting in larger models?
- 2 What is the impact of different task orders on forgetting?
- 3 How to optimize fine-tuning strategies to further mitigate forgetting?
Applications
Immediate Applications
Intelligent Customer Service Systems
By reducing knowledge forgetting, improve response accuracy and user satisfaction in customer service systems.
Personalized Recommendation Systems
Maintain accurate understanding of user preferences in continuously updated recommendation systems.
Long-term Vision
General Artificial Intelligence
Develop AI systems capable of continual learning without forgetting, enabling broader applications.
Abstract
Catastrophic forgetting (CF) is a phenomenon that occurs in machine learning when a model forgets previously learned information while acquiring new knowledge for achieving a satisfactory performance in downstream tasks. As large language models (LLMs) have demonstrated remarkable performance, it is intriguing to investigate whether CF exists during the continual instruction tuning of LLMs. This study empirically evaluates the forgetting phenomenon in LLMs' knowledge during continual instruction tuning from the perspectives of domain knowledge, reasoning, and reading comprehension. The experiments reveal that catastrophic forgetting is generally observed in LLMs ranging from 1b to 7b parameters. Surprisingly, as the model scale increases, the severity of forgetting intensifies in such a model sale range which may result from the much significant initial performance in the larger LLM. Comparing the decoder-only model BLOOMZ with the encoder-decoder model mT0, BLOOMZ exhibits less forgetting and retains more knowledge. Interestingly, we also observe that LLMs can mitigate language biases, such as gender bias, during continual fine-tuning. Furthermore, our findings indicate that general instruction tuning can help alleviate the forgetting phenomenon in LLMs during subsequent fine-tuning.