Academic

DistillLens: Symmetric Knowledge Distillation Through Logit Lens

arXiv:2602.13567v1 Announce Type: new Abstract: Standard Knowledge Distillation (KD) compresses Large Language Models (LLMs) by optimizing final outputs, yet it typically treats the teacher's intermediate layer's thought process as a black box. While feature-based distillation attempts to bridge this gap, existing methods (e.g., MSE and asymmetric KL divergence) ignore the rich uncertainty profiles required for the final output. In this paper, we introduce DistillLens, a framework that symmetrically aligns the evolving thought processes of student and teacher models. By projecting intermediate hidden states into the vocabulary space via the Logit Lens, we enforce structural alignment using a symmetric divergence objective. Our analysis proves that this constraint imposes a dual-sided penalty, preventing both overconfidence and underconfidence while preserving the high-entropy information conduits essential for final deduction. Extensive experiments on GPT-2 and Llama architectures dem

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Manish Dhakal, Uthman Jinadu, Anjila Budathoki, Rajshekhar Sunderraman, Yi Ding · · 1 min read · 3 views
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arXiv:2602.13567v1 Announce Type: new Abstract: Standard Knowledge Distillation (KD) compresses Large Language Models (LLMs) by optimizing final outputs, yet it typically treats the teacher's intermediate layer's thought process as a black box. While feature-based distillation attempts to bridge this gap, existing methods (e.g., MSE and asymmetric KL divergence) ignore the rich uncertainty profiles required for the final output. In this paper, we introduce DistillLens, a framework that symmetrically aligns the evolving thought processes of student and teacher models. By projecting intermediate hidden states into the vocabulary space via the Logit Lens, we enforce structural alignment using a symmetric divergence objective. Our analysis proves that this constraint imposes a dual-sided penalty, preventing both overconfidence and underconfidence while preserving the high-entropy information conduits essential for final deduction. Extensive experiments on GPT-2 and Llama architectures demonstrate that DistillLens consistently outperforms standard KD and feature-transfer baselines on diverse instruction-following benchmarks. The code is available at https://github.com/manishdhakal/DistillLens.

Executive Summary

The article 'DistillLens: Symmetric Knowledge Distillation Through Logit Lens' introduces a novel framework for knowledge distillation in Large Language Models (LLMs). Unlike standard KD methods that focus solely on final outputs, DistillLens aims to align the intermediate thought processes of teacher and student models by projecting hidden states into the vocabulary space via the Logit Lens. This approach uses a symmetric divergence objective to enforce structural alignment, preventing overconfidence and underconfidence while preserving high-entropy information. The study demonstrates that DistillLens outperforms traditional KD and feature-transfer methods across various benchmarks, using GPT-2 and Llama architectures. The code for DistillLens is publicly available, facilitating further research and application.

Key Points

  • ▸ DistillLens introduces a symmetric approach to knowledge distillation in LLMs.
  • ▸ The framework aligns intermediate thought processes by projecting hidden states into vocabulary space.
  • ▸ Symmetric divergence objective prevents overconfidence and underconfidence.
  • ▸ Extensive experiments show DistillLens outperforms standard KD and feature-transfer methods.
  • ▸ Code is publicly available for further research and application.

Merits

Innovative Approach

DistillLens introduces a novel method for knowledge distillation that goes beyond final output optimization, addressing the intermediate thought processes of models.

Empirical Validation

The study provides extensive experimental validation across different architectures and benchmarks, demonstrating the effectiveness of DistillLens.

Publicly Available Code

The availability of the code facilitates further research and practical application, promoting transparency and reproducibility.

Demerits

Complexity

The method introduces additional complexity compared to standard KD, which might require more computational resources and expertise to implement.

Generalizability

While the study shows promising results, the generalizability of DistillLens to other types of models and tasks remains to be fully explored.

Interpretability

The symmetric divergence objective, while effective, may not be easily interpretable, which could limit its adoption in certain contexts.

Expert Commentary

The article presents a significant advancement in the field of knowledge distillation, addressing a critical gap in standard KD methods. By focusing on the intermediate thought processes of models, DistillLens offers a more comprehensive approach to aligning teacher and student models. The use of a symmetric divergence objective is particularly noteworthy, as it effectively balances the trade-off between overconfidence and underconfidence, which is crucial for maintaining the reliability of model outputs. The empirical validation across different architectures and benchmarks lends credibility to the proposed method. However, the complexity introduced by DistillLens and the need for further exploration of its generalizability are important considerations. Overall, this study contributes valuable insights to the field and paves the way for more efficient and effective model compression techniques.

Recommendations

  • ✓ Further research should explore the generalizability of DistillLens to other types of models and tasks beyond LLMs.
  • ✓ Future studies could investigate the interpretability of the symmetric divergence objective and its implications for model transparency.

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