Decoder-based Sense Knowledge Distillation
arXiv:2602.22351v1 Announce Type: new Abstract: Large language models (LLMs) learn contextual embeddings that capture rich semantic information, yet they often overlook structured lexical knowledge such as word senses and relationships. Prior work has shown that incorporating sense dictionaries can improve knowledge distillation for encoder models, but their application to decoder as generative models remains challenging. In this paper, we introduce Decoder-based Sense Knowledge Distillation (DSKD), a framework that integrates lexical resources into the training of decoder-style LLMs without requiring dictionary lookup at inference time. Extensive experiments on diverse benchmarks demonstrate that DSKD significantly enhances knowledge distillation performance for decoders, enabling generative models to inherit structured semantics while maintaining efficient training.
arXiv:2602.22351v1 Announce Type: new Abstract: Large language models (LLMs) learn contextual embeddings that capture rich semantic information, yet they often overlook structured lexical knowledge such as word senses and relationships. Prior work has shown that incorporating sense dictionaries can improve knowledge distillation for encoder models, but their application to decoder as generative models remains challenging. In this paper, we introduce Decoder-based Sense Knowledge Distillation (DSKD), a framework that integrates lexical resources into the training of decoder-style LLMs without requiring dictionary lookup at inference time. Extensive experiments on diverse benchmarks demonstrate that DSKD significantly enhances knowledge distillation performance for decoders, enabling generative models to inherit structured semantics while maintaining efficient training.
Executive Summary
The article 'Decoder-based Sense Knowledge Distillation' introduces a novel framework called DSKD, which integrates lexical resources into the training of decoder-style large language models (LLMs). Unlike prior work that focused on encoder models, DSKD aims to enhance knowledge distillation by incorporating structured lexical knowledge such as word senses and relationships. The framework does not require dictionary lookup during inference, making it efficient. Extensive experiments demonstrate that DSKD significantly improves knowledge distillation performance for generative models, enabling them to inherit structured semantics while maintaining efficient training.
Key Points
- ▸ Introduction of Decoder-based Sense Knowledge Distillation (DSKD) framework
- ▸ Integration of lexical resources into decoder-style LLMs without inference-time dictionary lookup
- ▸ Significant improvement in knowledge distillation performance for generative models
- ▸ Maintenance of efficient training while inheriting structured semantics
Merits
Innovative Approach
The DSKD framework represents a novel approach to integrating lexical resources into decoder-style LLMs, addressing a gap in prior work that primarily focused on encoder models.
Efficiency
The framework's ability to incorporate structured lexical knowledge without requiring dictionary lookup at inference time enhances its practical applicability and efficiency.
Empirical Validation
Extensive experiments on diverse benchmarks demonstrate the significant improvement in knowledge distillation performance, providing robust empirical validation of the framework.
Demerits
Complexity
The integration of lexical resources into the training process may introduce additional complexity, which could be a barrier to adoption for some practitioners.
Generalizability
While the experiments show promising results, the generalizability of DSKD to other types of generative models and tasks remains to be fully explored.
Expert Commentary
The introduction of the Decoder-based Sense Knowledge Distillation (DSKD) framework represents a significant advancement in the field of large language models (LLMs). By addressing the gap in prior work that focused primarily on encoder models, DSKD provides a novel approach to integrating structured lexical knowledge into decoder-style LLMs. The framework's ability to enhance knowledge distillation performance without requiring dictionary lookup at inference time is particularly noteworthy, as it addresses a critical challenge in the efficient training of generative models. The extensive experiments conducted on diverse benchmarks provide robust empirical validation of the framework's effectiveness. However, the additional complexity introduced by the integration of lexical resources and the need for further exploration of its generalizability to other generative models and tasks are important considerations. Overall, DSKD offers a promising direction for improving the performance and efficiency of generative models, with potential implications for both practical applications and policy decisions in the field of AI.
Recommendations
- ✓ Further research should explore the generalizability of the DSKD framework to other types of generative models and tasks to ensure its broad applicability.
- ✓ Practitioners should consider adopting the DSKD framework to enhance the performance of their generative models, particularly in applications requiring high-quality language processing.
