SEEKR: A Novel Method for Data-Efficient Continual Learning in Large Language Models by Selectively Retaining Knowledge from Important Attention Heads

核心概念

SEEKR is a novel continual learning method for large language models that addresses catastrophic forgetting by selectively distilling knowledge from important attention heads, resulting in improved data efficiency and performance.

摘要

• Bibliographic Information: He, J., Guo, H., Zhu, K., Zhao, Z., Tang, M., Wang, J. (2024). SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models. arXiv preprint arXiv:2411.06171v1.
• Research Objective: This paper introduces SEEKR, a new approach for continual learning in large language models (LLMs) that aims to mitigate catastrophic forgetting while improving data efficiency.
• Methodology: SEEKR leverages a selective attention-guided knowledge retention mechanism. It identifies and distills knowledge from the most important attention heads in the LLM, which are determined by considering both their task sensitivity and forgettability during sequential training on new tasks. This selective distillation process allows for more efficient use of replay data from previous tasks. The method is evaluated on two continual learning benchmarks: TRACE, specifically designed for LLMs, and SuperNI, which includes traditional NLP tasks.
• Key Findings: Experimental results demonstrate that SEEKR outperforms existing continual learning methods on both benchmarks, achieving higher overall performance and lower backward transfer (forgetting). Importantly, SEEKR exhibits superior data efficiency, achieving comparable or better results with significantly less replay data (as low as 1%) compared to other methods.
• Main Conclusions: SEEKR offers a promising solution for continual learning in LLMs, effectively mitigating catastrophic forgetting while requiring fewer resources. The selective attention distillation mechanism proves to be a key factor in its success.
• Significance: This research contributes to the growing field of continual learning for LLMs, addressing the critical challenge of maintaining performance on previously learned tasks while adapting to new ones. The proposed method's data efficiency has significant implications for real-world applications where storage and computational resources are limited.
• Limitations and Future Research: The authors acknowledge limitations regarding the applicability of SEEKR in privacy-sensitive scenarios where historical data access is restricted. Future research could explore combining SEEKR with pseudo-sample generation techniques to address this. Additionally, further investigation is needed to evaluate SEEKR's performance on larger-scale LLMs and its potential for continual learning in multimodal settings.

统计

SEEKR achieves comparable or better performance with only 1/10 of the replayed data used by other methods.
SEEKR reduces the proportion of replayed data to 1%.
With a fixed layer budget of 24, the performance improvement of SEEKR plateaus at a head budget of 128.
The performance of SEEKR improves with an increasing layer budget and reaches its optimum at 24.
At a replay ratio of 10%, the BWT score of SEEKR exceeds 0, indicating no forgetting or even a positive transfer.

引用

"However, existing methods fail to fully exploit the knowledge embedded in models from previous tasks, resulting in the need for a relatively large number of replay samples to achieve good results."
"Grafting the attention weights from the LLM of the old tasks to the final LLM after continual learning can maintain better performance on old tasks, which suggests that the attention weights could be crucial to alleviate the catastrophic forgetting problem and achieve more comprehensive knowledge retention."
"Extensive experiments validate the superiority of SEEKR, showcasing its data efficiency by using just 1% of replay samples to achieve the comparable or better performance that other methods reach with 10% of replay samples."

从中提取的关键见解

SEEKR: Selective Attention-Guided Knowledge Retention for Continual Learning of Large Language Models

by Jinghan He, ... 在 arxiv.org 11-12-2024

https://arxiv.org/pdf/2411.06171.pdf

更深入的查询

How might the principles of SEEKR be applied to other deep learning architectures beyond LLMs, particularly those heavily reliant on attention mechanisms?

SEEKR's core principles are built around the importance of attention mechanisms in knowledge retention for continual learning. This makes it highly applicable to other deep learning architectures beyond LLMs, especially those where attention plays a crucial role. Here's how:

Computer Vision (CV): Transformer-based architectures like Vision Transformers (ViTs) have become increasingly popular in CV. SEEKR's attention distillation and importance measures could be directly applied to these models. For instance, in continual object recognition, SEEKR could identify and preserve attention heads crucial for distinguishing previously learned objects while adapting to new ones.
Time Series Analysis: Recurrent Neural Networks (RNNs) with attention mechanisms are widely used for tasks like natural language processing and speech recognition. SEEKR's principles could be adapted to identify and retain important temporal dependencies captured by the attention heads, enabling the model to learn new sequences without forgetting past patterns.
Multi-Modal Learning: Models dealing with multiple data modalities, like image captioning or visual question answering, often employ attention to fuse information across modalities. SEEKR's approach could be extended to identify and preserve attention heads crucial for inter-modal understanding, facilitating continual learning in such complex scenarios.
The key adaptation would involve tailoring the head importance measures (task sensitivity and forgettability) to the specific domain and task. This might involve incorporating domain-specific knowledge or metrics relevant to the problem.

Could the reliance on a pre-defined budget for selecting important attention heads limit the adaptability of SEEKR in dynamically changing learning environments where the importance of tasks might shift over time?

You are right to point out that SEEKR's reliance on a pre-defined budget for selecting important attention heads could pose a limitation in highly dynamic learning environments.
Here's why:

Static Budgets: The current implementation of SEEKR uses fixed budgets (layer budget BL, head budget BH) for selecting attention heads. This assumes a relatively stable understanding of task importance throughout the continual learning process.
Shifting Task Importance: In dynamic environments, the importance of tasks might change. New tasks could be more closely related to some older tasks, rendering certain previously important attention heads less relevant. Conversely, previously less important heads might become crucial.
To address this limitation, several research directions could be explored:

Dynamic Budget Allocation: Instead of fixed budgets, explore mechanisms for dynamically adjusting the number of heads selected for distillation based on the characteristics of the new task and its relationship to previous tasks. This could involve online meta-learning strategies or reinforcement learning to optimize budget allocation over time.
Importance Measure Refinement: Incorporate a temporal dimension into the head importance measures. For example, the forgettability measure could be weighted by the recency of the task or the current relevance of the knowledge it represents.
Adaptive Attention Span: Instead of randomly selecting queries for distillation, investigate mechanisms for dynamically adjusting the attention span based on the complexity of the input and the task at hand. This could involve techniques like attention over attention or dynamic routing.

If we view the selective attention mechanism in SEEKR as a form of artificial memory consolidation, what insights from neuroscience could further enhance the model's ability to retain and integrate knowledge over long periods of continual learning?

Viewing SEEKR's selective attention as a form of artificial memory consolidation opens up exciting avenues for improvement inspired by neuroscience:

Targeted Replay: The brain doesn't replay memories randomly. Instead, it prioritizes the consolidation of salient and emotionally charged experiences. SEEKR could incorporate similar mechanisms by prioritizing the replay of data samples or attention patterns associated with high task sensitivity or those that led to significant changes in the model's understanding.
Sleep and Offline Consolidation: Neuroscience has shown that sleep plays a crucial role in memory consolidation, transferring information from the hippocampus (short-term memory) to the neocortex (long-term memory). SEEKR could benefit from incorporating similar offline consolidation phases where the model revisits and integrates knowledge from previous tasks without being exposed to new information.
Neuromodulation and Plasticity: The brain uses neuromodulators like dopamine to regulate synaptic plasticity, strengthening important connections. SEEKR could explore analogous mechanisms by dynamically adjusting the learning rates or distillation weights of important attention heads based on their contribution to task performance.
Complementary Learning Systems: The brain employs multiple memory systems, with the hippocampus rapidly encoding new information and the neocortex gradually integrating it into existing knowledge. SEEKR could explore similar architectures by incorporating a separate module for quickly adapting to new tasks while preserving a core set of attention heads representing consolidated knowledge.
By drawing inspiration from these neuroscientific principles, SEEKR and other continual learning methods can move towards more robust and efficient knowledge retention, paving the way for artificial agents capable of lifelong learning.