Proxy-KD: Enhancing Knowledge Distillation from Black-Box LLMs Using Proxy Models

Core Concepts

Proxy-KD, a novel knowledge distillation method, effectively transfers knowledge from black-box LLMs to smaller models by employing an aligned proxy model, outperforming traditional black-box and white-box techniques.

Abstract

Bibliographic Information:

Chen, H., Chen, R., Yi, Y., Quan, X., Li, C., Yan, M., & Zhang, J. (2024). Knowledge Distillation of Black-Box Large Language Models. arXiv preprint arXiv:2401.07013.

Research Objective:

This paper addresses the challenge of knowledge distillation from black-box Large Language Models (LLMs) where internal states are inaccessible, aiming to improve the performance of smaller, open-source models by leveraging the capabilities of these powerful, proprietary LLMs.

Methodology:

The researchers propose Proxy-KD, a two-stage method involving: 1) Proxy Model Alignment: A white-box LLM (proxy) is aligned with the black-box teacher LLM through supervised fine-tuning and preference optimization using the Direct Preference Optimization (DPO) algorithm. 2) Student Knowledge Distillation: The student model learns from both hard labels (outputs) of the black-box teacher and soft labels (output distributions) provided by the aligned proxy, incorporating a sample-level weight in the distillation objective to prioritize well-aligned distributions.

Key Findings:

• Proxy-KD consistently outperforms both black-box and white-box KD methods across various benchmarks, including BBH, AGIEval, ARC, CSQA, GSM8K, and MMLU.
• Proxy model alignment, particularly with preference optimization, is crucial for effective knowledge transfer.
• Larger and more robust proxy models tend to align better with black-box teachers, leading to improved distillation performance.
• A sample-level weighted KL loss function further enhances the student model's learning by focusing on well-aligned distributions from the proxy.

Main Conclusions:

Proxy-KD presents a compelling solution for distilling knowledge from advanced black-box LLMs, effectively bridging the gap between proprietary and open-source models. The method leverages the strengths of both black-box and white-box KD while mitigating their limitations.

Significance:

This research contributes significantly to the field of natural language processing by enabling the development of more capable and efficient open-source LLMs, potentially democratizing access to advanced language processing capabilities.

Limitations and Future Research:

The study acknowledges limitations regarding training time overhead due to proxy alignment and preference optimization. Future research could explore more efficient alignment strategies and investigate the impact of different proxy model architectures and sizes on distillation effectiveness. Additionally, expanding experiments to include other LLM backbones beyond the Llama series would provide a more comprehensive evaluation of Proxy-KD's generalizability.

Stats

The training corpus consists of 1 million output sequences generated by GPT-4, combining OpenOrca and Nectar datasets, and synthetic data.
The training data is split into three parts: 10% for warm-up, 45% for proxy alignment, and 45% for student distillation.
Proxy-KD achieves a score of 53.40 on the BBH dataset and 53.07 on the GSM8K dataset, outperforming larger models trained with traditional black-box KD.
Removing the proxy model leads to performance drops of 4.24 on ARC, 6.72 on BBH, and 3.56 on GSM8K.
Skipping proxy alignment results in a performance decrease of 10.40 on BBH, 5.53 on GSM8K, and 3.26 on MMLU.
Removing preference optimization from proxy alignment leads to performance drops on BBH and MMLU.

Quotes

"Proxy-KD introduces a proxy model, typically a white-box LLM, between the student and the black-box teacher."
"Our experiments show that Proxy-KD not only enhances the performance of KD from black-box teacher models but also surpasses traditional white-box KD techniques."
"This approach presents a compelling new avenue for distilling knowledge from advanced LLMs."

Key Insights Distilled From

Knowledge Distillation of Black-Box Large Language Models

by Hongzhan Che... at arxiv.org 11-12-2024

https://arxiv.org/pdf/2401.07013.pdf

Deeper Inquiries

How might Proxy-KD be adapted for multi-modal tasks involving both text and images?

Adapting Proxy-KD for multi-modal tasks like image captioning or visual question answering would require several key modifications to handle both text and image inputs:

Multi-modal Proxy Model: Instead of a text-only LLM, the proxy model would need to be a multi-modal model capable of processing and understanding both text and images. This could involve architectures like Vision Transformers (ViTs) or CLIP, which learn joint representations of text and images.

Multi-modal Alignment: The proxy alignment process would need to be adapted for multi-modal inputs. This could involve using a combination of:

Image-Text Datasets: Training on datasets containing aligned image-text pairs, similar to how text-only datasets are used in the current Proxy-KD.
Contrastive Learning: Employing contrastive learning objectives to align the image and text embeddings generated by the proxy model, ensuring they capture the semantic relationships between the two modalities.

Multi-modal Distillation: The knowledge distillation process would need to handle both text generation and image understanding. This could involve:

Separate Objectives: Using separate loss functions for text generation (e.g., cross-entropy, KL divergence) and image understanding (e.g., contrastive loss, image classification loss).
Joint Representation Distillation: Encouraging the student model to learn a joint representation of text and images similar to the proxy model, potentially by distilling intermediate layer activations or attention maps.

Evaluation on Multi-modal Benchmarks: Evaluating the adapted Proxy-KD on established multi-modal benchmarks like COCO Captioning, VQA, or image retrieval tasks to assess its effectiveness in transferring knowledge from the black-box teacher to the student model.

By incorporating these adaptations, Proxy-KD could potentially be extended to leverage the knowledge of powerful, black-box multi-modal models for improving the performance of smaller, more efficient multi-modal student models.

Could the reliance on a large proxy model in Proxy-KD be mitigated by using ensemble methods with smaller, specialized models?

Yes, the reliance on a single large proxy model in Proxy-KD could potentially be mitigated by using an ensemble of smaller, specialized models. This approach could offer several advantages:

Reduced Computational Overhead: Training and deploying an ensemble of smaller models can be computationally less demanding than using a single large model, especially for resource-constrained settings.

Specialized Knowledge Capture: Instead of relying on a single general-purpose proxy, an ensemble could consist of smaller models specialized in different aspects of the black-box teacher's capabilities. For example, one model could focus on factual language understanding, while another specializes in creative text generation.

Ensemble Distillation: The knowledge distillation process could be adapted to leverage the strengths of each specialized model in the ensemble. This could involve:

Weighted Averaging: Combining the output distributions of different ensemble members using weighted averaging, where the weights reflect their performance on specific tasks or aspects of the data.
Knowledge Distillation from Multiple Teachers: Extending Proxy-KD to handle multiple teacher models, where each ensemble member acts as a specialized teacher for the student model.

Modular Design and Scalability: An ensemble approach allows for a more modular and scalable system. New specialized models can be added to the ensemble as needed to address specific limitations or capture new capabilities of the black-box teacher.

However, challenges like effectively training and combining the outputs of specialized models, as well as potential increases in complexity for managing and deploying the ensemble, would need to be carefully addressed.

How can the ethical implications of distilling knowledge from potentially biased black-box LLMs be addressed in the context of Proxy-KD?

Distilling knowledge from black-box LLMs raises ethical concerns, particularly regarding potential biases present in the teacher model. These biases can be amplified in the student model, perpetuating harmful stereotypes or unfair outcomes. Addressing these ethical implications in Proxy-KD requires a multi-faceted approach:

Bias Awareness and Mitigation during Proxy Alignment:

Diverse and Representative Data: Use a diverse and representative dataset for proxy alignment to minimize the risk of replicating or amplifying existing biases from the black-box teacher.
Bias Detection and Evaluation: Incorporate bias detection methods during the proxy alignment phase to identify and quantify potential biases in the proxy model's outputs.
Adversarial Training: Employ adversarial training techniques to make the proxy model more robust to biased inputs and less likely to generate biased outputs.

Ethical Considerations during Knowledge Distillation:

Selective Distillation: Instead of blindly distilling all knowledge, focus on transferring desirable capabilities while mitigating the transfer of biases. This could involve carefully selecting training data or using techniques like importance weighting to emphasize specific aspects of the proxy's knowledge.
Fairness Constraints: Introduce fairness constraints during student model training to explicitly penalize biased outputs and promote fairness across different demographic groups.
Transparency and Explainability: Strive for transparency in the Proxy-KD process by documenting the training data, proxy model architecture, and evaluation metrics. This transparency can help identify potential sources of bias and facilitate the development of mitigation strategies.

Ongoing Monitoring and Evaluation:

Continuous Bias Monitoring: Continuously monitor the student model's outputs for potential biases, even after deployment. This could involve using automated tools or human evaluation to detect and address emerging biases.
Iterative Improvement: Treat bias mitigation as an ongoing process, iteratively refining the Proxy-KD pipeline and retraining the student model as new biases are identified or new mitigation techniques become available.

Addressing the ethical implications of Proxy-KD requires a proactive and continuous effort to ensure that the distilled knowledge is used responsibly and does not perpetuate harmful biases.