Self-Data Distillation for Recovering Quality in Pruned Large Language Models: A Study on Llama3.1-8B Instruct

核心概念

Self-data distillation, a novel fine-tuning technique leveraging the original unpruned model to generate a distilled dataset, effectively mitigates quality degradation in pruned large language models, outperforming standard supervised fine-tuning methods.

要約

• Bibliographic Information: Thangarasa, V., Venkatesh, G., Sinnadurai, N., & Lie, S. (2024). Self-Data Distillation for Recovering Quality in Pruned Large Language Models. arXiv preprint arXiv:2410.09982.
• Research Objective: This paper introduces self-data distillation, a novel fine-tuning method designed to recover the quality of large language models (LLMs) after structured pruning, specifically focusing on mitigating catastrophic forgetting.
• Methodology: The researchers employed a layer-pruning algorithm based on angular cosine distance to identify and remove redundant layers in the Llama3.1-8B Instruct model. They then compared the effectiveness of self-data distilled fine-tuning against standard supervised fine-tuning and no fine-tuning across various pruning block sizes and datasets, including OpenMathInstruct, GSM8k, Dolly, and Alpaca. Model quality was evaluated using the HuggingFace OpenLLM Leaderboard v1, focusing on reasoning-heavy tasks like ARC-C, GSM8k, and MMLU.
• Key Findings: Self-data distillation consistently outperformed standard supervised fine-tuning and no fine-tuning in recovering model quality after pruning. The method proved particularly effective for reasoning-intensive tasks and demonstrated scalability with larger datasets leading to better quality recovery. Notably, merging models fine-tuned on different datasets using Spherical Linear Interpolation (SLERP) further enhanced quality retention.
• Main Conclusions: Self-data distilled fine-tuning offers a practical and effective solution for mitigating quality degradation in pruned LLMs. The technique helps preserve the original model's learned distribution, reducing catastrophic forgetting and maintaining accuracy across various tasks.
• Significance: This research significantly contributes to the field of model compression for LLMs, providing a valuable technique to balance model size and performance. This is particularly relevant for deploying LLMs on resource-constrained devices.
• Limitations and Future Research: While the study focuses on Llama3.1-8B Instruct, future research could explore the effectiveness of self-data distillation on other LLM architectures and across a wider range of tasks. Additionally, combining self-data distillation with other model compression techniques like quantization or knowledge distillation could further enhance efficiency without compromising accuracy.

統計

Pruning 6 decoder blocks on Llama3.1-8B Instruct (reducing the model size from 8.03B to 6.72B parameters) with self-data distillation retained 91.2% of the original model's accuracy compared to 81.7% with standard supervised fine-tuning, while reducing real-world FLOPs by 16.30%.
Self-data distillation improved average accuracy by up to 8% over standard supervised fine-tuning on the HuggingFace OpenLLM Leaderboard v1.
Fine-tuning with a self-data distilled 50k-sample OpenMathInstruct dataset resulted in a higher mean embedding similarity score of 0.92 compared to 0.83 for standard supervised fine-tuning, indicating better preservation of the original model's learned representations and reduced catastrophic forgetting.

引用

"To our knowledge, we are the first to introduce self-data distillation as a fine-tuning method for recovering the model quality of pruned models."
"Empirically, we show that self-data distillation on Llama3.1-8B Instruct consistently outperforms SFT across all pruned models."
"We demonstrate that self-data distillation scales effectively across a wide range of open-source fine-tuning datasets for LLMs, covering open-domain conversation, reasoning, and instruction following, with quality recovery significantly improving as the dataset size increases."

抽出されたキーインサイト

Self-Data Distillation for Recovering Quality in Pruned Large Language Models

by Vithursan Th... 場所 arxiv.org 10-15-2024

https://arxiv.org/pdf/2410.09982.pdf

深掘り質問

How does the performance of self-data distillation compare to other model compression techniques like knowledge distillation or quantization when applied to pruned LLMs?

While the paper focuses primarily on comparing self-data distillation to supervised fine-tuning (SFT) as a post-pruning strategy for LLMs, it doesn't directly compare it with other model compression techniques like knowledge distillation (KD) or quantization. However, we can infer some potential advantages and disadvantages based on existing literature and the paper's findings:
Potential Advantages of Self-Data Distillation:

Directly Addresses Pruning Challenges: Unlike KD or quantization, which are generally applied to compress a model without structural changes, self-data distillation is specifically designed to mitigate the quality degradation caused by structured pruning. It achieves this by aligning the fine-tuning dataset with the pruned model's architecture and knowledge representation.
Reduced Catastrophic Forgetting: The paper demonstrates that self-data distillation is more effective than SFT in mitigating catastrophic forgetting, which is crucial for preserving the pruned model's performance on a wide range of tasks. This advantage could potentially extend to scenarios where KD or quantization might exacerbate forgetting.
Potential Disadvantages of Self-Data Distillation:

Reliance on Original Model: Self-data distillation requires access to the original, unpruned model to generate the distilled dataset. This could be a limitation compared to KD or quantization, which can be applied directly to the pruned model.
Limited Compression Compared to KD/Quantization: Self-data distillation primarily focuses on recovering quality loss after pruning. It might not offer the same level of compression as KD, which can train significantly smaller student models, or quantization, which reduces model size by representing weights and activations with lower precision.
Synergy with Other Techniques:
It's important to note that self-data distillation, KD, and quantization are not mutually exclusive. Combining these techniques could potentially lead to even greater compression and quality retention. For instance:

Self-Data Distillation + KD: Applying KD after self-data distillation could further improve generalization and potentially allow for training even smaller student models.
Self-Data Distillation + Quantization: Quantizing the pruned and self-data distilled model could further reduce its size and inference latency.
Further research is needed to thoroughly compare self-data distillation with other compression techniques and explore their potential synergies in the context of pruned LLMs.

Could the reliance on the original unpruned model for generating the distilled dataset pose limitations in scenarios where access to the original model is restricted due to privacy or proprietary concerns?

Yes, the reliance on the original unpruned model for generating the distilled dataset in self-data distillation could indeed pose limitations in scenarios with restricted access due to privacy or proprietary concerns.
Here's why:

Data Leakage: The distilled dataset, while not directly revealing the original model's parameters, might still contain information that could be used to infer sensitive details about the original training data or the model's internal representations. This is particularly relevant in privacy-sensitive domains like healthcare or finance.
Proprietary Model Access: If the original unpruned model is owned by a third party and access is restricted, applying self-data distillation becomes infeasible. This limits the applicability of the technique in scenarios where collaboration or knowledge sharing is limited.
Potential Mitigations:

Differential Privacy: Techniques like differential privacy could be explored to add noise to the distilled dataset generation process, making it harder to infer sensitive information about the original model or training data.
Federated Learning: In cases where the original model is distributed across multiple devices, federated learning could be employed to generate the distilled dataset collaboratively without requiring direct access to the centralized model.
Alternative Distillation Strategies: Exploring alternative distillation strategies that rely on publicly available or less sensitive teacher models could be a viable direction for future research.
Addressing these limitations is crucial for broader adoption of self-data distillation, especially in privacy-sensitive domains or when dealing with proprietary models.

If we view the evolution of language models as a form of knowledge compression itself, how can we leverage the principles of self-data distillation to improve human learning and knowledge transfer?

Viewing the evolution of language models as a form of knowledge compression provides a fascinating lens through which to examine human learning. Here's how we might leverage the principles of self-data distillation to enhance human learning and knowledge transfer:
1. Personalized Learning Paths:

"Unpruned Model" as Existing Knowledge: Consider an individual's current knowledge base as the "unpruned model."
"Distilled Dataset" as Tailored Learning Material: Educational materials could be tailored to generate a "distilled dataset" that bridges the gap between existing knowledge and desired learning outcomes. This involves identifying key concepts, simplifying complex information, and presenting it in a way that resonates with the individual's learning style and pace.
2. Active Recall and Elaboration:

"Self-Data Generation" as Active Recall: Encourage learners to actively recall and summarize information in their own words, similar to how the "unpruned model" generates responses in self-data distillation.
"Distillation" as Elaboration and Synthesis: Promote elaboration by asking learners to connect new information to existing knowledge, provide examples, and explain concepts in different ways. This process mirrors the "distillation" step, where the model refines its understanding through generating responses.
3. Collaborative Knowledge Distillation:

"Model Merging" as Collaborative Learning: Facilitate collaborative learning environments where individuals with diverse perspectives and expertise can share their understanding of a topic. This mirrors the "model merging" aspect, where different models contribute to a more comprehensive and robust understanding.
"Distilled Dataset" as Shared Knowledge Base: Encourage the creation of shared knowledge repositories where learners can contribute summaries, explanations, and examples, creating a continuously evolving "distilled dataset" for the community.
4. Metacognitive Awareness and Feedback:

Monitoring "Distribution Shift" as Metacognition: Help learners develop metacognitive skills to monitor their own learning process, identify areas where their understanding might be "drifting" from the intended learning goals, and adjust their learning strategies accordingly.
"Fine-tuning" as Feedback and Reflection: Provide regular feedback and opportunities for reflection to help learners "fine-tune" their understanding, address misconceptions, and solidify their knowledge.
By applying these principles, we can potentially create more effective and engaging learning experiences that are tailored to individual needs and promote deeper understanding and knowledge retention.