Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning

Conceptos Básicos

The author explores the mode connectivity phenomenon in continual learning scenarios for large language models, aiming to strike a balance between plasticity and stability through innovative methods like I-LoRA.

Resumen

The content delves into the issue of catastrophic forgetting in large language models (LLMs) during continual fine-tuning. It introduces the concept of mode connectivity and proposes I-LoRA as a method to address this challenge. Through experiments on domain-specific CL benchmarks, I-LoRA consistently outperforms previous state-of-the-art approaches, showcasing significant improvement in performance gains. The study highlights the importance of balancing plasticity and stability in LLMs for effective continual learning.
Plenty of existing works have explored strategies like memory replay, regularization, and parameter isolation to mitigate catastrophic forgetting. However, little is known about the geometric connection of various minima in continual LLMs fine-tuning scenarios. The study investigates mode connectivity through experiments and proposes I-LoRA as an effective method based on LoRA parameter interpolations. Extensive analysis on diverse CL benchmarks demonstrates significant improvement over previous approaches, providing insights for future research on large language model continual learning problems.
The content also discusses related works on continual learning methodologies such as replay-based methods, regularization-based methods, and parameter isolation methods. It further explores linear mode connectivity as a phenomenon where different minima can be connected by low-loss paths in the parameter space.

Estadísticas

Extensive experiments demonstrate up to 11% performance gains with I-LoRA.
Eight domain-specific CL benchmarks were used for analysis.
Weight distance metrics were utilized to evaluate memorization effects.
Centered Kernel Alignment was used to assess representation similarity.
Embedding landscape visualization illustrated geometric characteristics of loss landscapes.

Citas

"The proposed I-LoRA consistently outperforms previous methods and shows remarkable improvement over the previous state-of-the-art CL methods."
"I-LoRA achieves a nuanced trade-off between plasticity and stability by leveraging two independent modules functioning as fast and slow learners."

Ideas clave extraídas de

Analyzing and Reducing Catastrophic Forgetting in Parameter Efficient Tuning

by Weijieying R... a las arxiv.org 03-01-2024

https://arxiv.org/pdf/2402.18865.pdf

Consultas más profundas

How does mode connectivity impact long-term memorization ability in large language models?

Mode connectivity plays a crucial role in enhancing the long-term memorization ability of large language models (LLMs) during continual learning. By connecting different minima through low-loss valleys, mode connectivity allows for a smooth transition between optimal regions for various tasks. This phenomenon enables LLMs to strike a balance between stability and plasticity, ensuring that they can adapt to new tasks while preserving previously learned knowledge.
In the context of continual fine-tuning for LLMs, leveraging mode connectivity facilitates the establishment of parametric paths that connect historical and current optima. These paths enable the model parameters to traverse along trajectories that optimize performance on both previous and new tasks. By interpolating between adjacent minima based on mode connectivity, LLMs can achieve an optimal trade-off between stability (retaining historical knowledge) and plasticity (adapting to new information).
Overall, mode connectivity enhances the memorization ability of LLMs by providing a geometric framework through which different task optima are interconnected. This allows for smoother transitions between tasks, leading to improved long-term retention of knowledge.

How can insights from geometric connections of minima be applied beyond large language models?

Insights from geometric connections of minima can be applied beyond large language models (LLMs) in various domains where continual learning is essential. Here are some ways these insights can be leveraged:

Computer Vision: Similar observations about linear mode connectivity have been made in computer vision research as well. The concept of connecting different minima through low-loss paths can help improve model performance in scenarios involving sequential learning or multitask learning.

Reinforcement Learning: In reinforcement learning settings, understanding how different policy optima are connected could lead to more efficient transfer learning across tasks without catastrophic forgetting.

Natural Language Processing: Beyond LLMs, other NLP models could benefit from exploring geometric connections between task-specific optima when adapting to new datasets or downstream tasks.

Multi-Task Learning: Insights into how adjacent minima are connected could enhance multi-task learning approaches by guiding parameter updates towards regions with minimal loss changes across multiple tasks.

General Machine Learning Applications: The principles behind geometric connections of minima offer valuable guidance for developing algorithms that balance adaptation to new data with retention of prior knowledge in any machine learning setting requiring continual learning capabilities.

What are potential limitations or challenges associated with leveraging mode connectivity for continual learning?

While leveraging mode connectivity offers significant benefits for improving performance in continual learning scenarios, there are several limitations and challenges to consider:

Computational Complexity: Analyzing and exploiting mode connectivity may require significant computational resources due to the need for extensive experiments and analysis across diverse datasets.

2 .Interpolation Sensitivity: Determining the optimal interpolation factor λ when traversing along parametric paths connecting different task optima may introduce sensitivity issues where small variations could significantly impact model performance.
3 .Overfitting Concerns: Depending too heavily on interpolations based on mode connectivity may risk overfitting specific patterns present within training data rather than generalizing well across unseen data.
4 .Generalizability: While effective within certain contexts like large language models (LLMs), applying insights from geometric connections beyond these specific domains may require careful adaptation due to differences in data characteristics and model architectures.
5 .Theoretical Understanding: Despite empirical evidence supporting its effectiveness, further theoretical analysis is needed to fully understand how leveraging mode connectivity impacts model behavior over time.
These limitations highlight the importance of carefully considering practical implications when utilizing insights from geometric connections for continual learning applications outside specific domains like LLMs."