Quantification of Large Language Model Distillation (2501.12619v3)

Abstract: Model distillation is a fundamental technique in building LLMs, transferring knowledge from a teacher model to a student model. However, distillation can lead to model homogenization, reducing diversity among models and impairing their ability to robustly handle complex or novel tasks. These limitations underscore the need to systematically quantify the distillation process and its impact. In this work, we propose a framework to evaluate and quantify model distillation. Our method addresses two key aspects: (1) Identifying identity cognition contradictions to assess discrepancies in how models perceive and represent identity-related information, and (2) Analyzing multi-granularity response similarities across models to measure the extent of homogenization. Experimental results demonstrate two key insights: (1) Well-known closed-source and open-source LLMs usually exhibit high distillation degrees, except for Claude, Doubao, and Gemini. (2) Base LLMs show higher distillation degrees compared to aligned LLMs. By offering a systematic approach to improve the transparency of LLM data distillation, we call for LLMs with more independent development and more transparent technical reports to improve LLMs' robustness and safety. The code and data are available under https://github.com/Aegis1863/LLMs-Distillation-Quantification.

• The paper presents a novel framework quantifying LLM distillation using Response Similarity Evaluation (RSE) and Identity Consistency Evaluation (ICE) techniques.
• The experiments highlight distinct distillation levels across models, with base LLMs showing higher homogenization than aligned or proprietary counterparts.
• The findings advocate for transparent reporting and independent development strategies to mitigate distillation-induced performance limitations in LLMs.

Distillation Quantification for LLMs

The paper "Distillation Quantification for LLMs" addresses the increasingly pivotal role of model distillation in optimizing LLMs by proposing a comprehensive framework for evaluating and quantifying model distillation. In the context of LLMs, model distillation involves transferring the knowledge of more extensive, robust LLMs to smaller ones, thereby achieving performance efficiency without substantial computational expenses. However, this technique often leads to homogenization, adversely affecting models' capabilities to manage intricate or novel tasks. This research notably contributes to the field by introducing a systematized approach to assess the extent and implications of model distillation.

Primary Contributions

The authors propose two pioneering methods: Response Similarity Evaluation (RSE) and Identity Consistency Evaluation (ICE), which serve as comprehensive metrics for assessing distillation levels in LLMs:

1. Response Similarity Evaluation (RSE): This method evaluates the similarity of responses between the test models and a reference LLM, such as GPT-4. By analyzing response style, logical structure, and content detail, RSE provides a fine-grained view of how closely distillation aligns student models with teacher models. The results are particularly notable as they highlight variations in distillation degrees across different LLMs, with some demonstrating low homogenization levels, indicative of more significant independence from the referenced models.
2. Identity Consistency Evaluation (ICE): This method utilizes a tailored framework, GPTFuzz, to ascertain identity cognition consistency under possible jailbreak attacks. By initiating iterative prompt crafting, ICE assesses models' vulnerabilities in maintaining consistent self-awareness, particularly distinguishing between erroneous identity declarations caused by distillation information leakage and legitimate responses. ICE emphasizes the importance of robustness against adversarial inputs as a critical factor in evaluating model independence.

Experimental Insights

The experimental results provide critical insights into the prevalence of distillation across LLMs:

• Distillation Variance: The research indicates a marked difference in distillation levels among leading LLMs, with Claude, Gemini, and other models showcasing lower distillation degrees compared to their counterparts. This reflects potentially more independent developmental approaches and highlights the need for transparency and robust reporting in model development.
• Base vs. Aligned LLMs: Base LLMs were observed to exhibit higher distillation levels compared to aligned LLMs. This finding emphasizes the impact of alignment processes in enhancing the independent capabilities of LLMs, thus reducing their susceptibility to homogenization.
• Closed vs. Open-source Models: The paper also identifies variations between closed-source and open-source models, with results suggesting that proprietary innovations or distinctions in available training corpora might mitigate distillation effects better than open-source projects.

Implications and Future Directions

The implications of this research are significant for the development and deployment of LLMs:

• Development Independence: There is a clear call for enhancing the independence of LLM development practices to mitigate the detrimental effects associated with excessive distillation. By fostering more diverse LLM architectures and training methodologies, the robustness of these models can be considerably improved.
• Transparent Reporting: The authors advocate for more transparent technical reports in model development to better quantify and communicate the extent of distillation and its potential impacts on model performance across various domains.
• Systematic Evaluation Frameworks: The proposed framework sets a precedent for future research on systematic evaluation metrics. It could extend beyond LLMs to other domains of AI, improving the understanding and transparency of model behaviors linked to distillation processes.

In conclusion, the paper presents significant advancements in understanding model distillation through robust analytical frameworks. This research paves the way for more transparent and independent development strategies in the field of LLMs, underscoring the need for cautious and strategic use of distillation techniques. As AI continues to evolve, such comprehensive assessments remain indispensable in ensuring the development of efficient, safe, and reliable models.