LLM Modules: Knowledge Transfer from a Large to a Small Model using Enhanced Cross-Attention (2502.08213v1)

Abstract: In this work, we propose an architecture of LLM Modules that enables the transfer of knowledge from a large pre-trained model to a smaller model using an Enhanced Cross-Attention mechanism. In the proposed scheme, the Qwen2-1.5B model is frozen and its representations are passed through specially designed attention layers to the GPT-Neo-125M model, which is trained on limited computational resources. Experimental results on the Bespoke-Stratos-17k dataset demonstrate that after 15 epochs of training, the combined model generates responses comparable in quality to those obtained by distillation. We discuss the advantages of the modular approach, provide examples of input queries and comparative analysis, and outline prospects for further extension of the method.

• The paper introduces LLM Modules that transfer robust representations from a large (Qwen2-1.5B) model to a smaller (GPT-Neo-125M) model using enhanced cross-attention layers.
• It leverages linear projections, adapter blocks, and a gating mechanism to align and blend information effectively between models.
• Experiments on the Bespoke-Stratos-17k dataset demonstrate significant reductions in training and validation loss, highlighting improved generation quality and efficiency.

Overview of LLM Modules: Knowledge Transfer from a Large to a Small Model using Enhanced Cross-Attention

In recent times, the paradigm of leveraging LLMs for natural language processing tasks has emerged as a dominant strategy. However, the substantial computational and resource requirements inherent in such models pose significant limitations, particularly for smaller-scale applications. The paper, "LLM Modules: Knowledge Transfer from a Large to a Small Model using Enhanced Cross-Attention," introduces a novel architectural framework aimed at mitigating these challenges by facilitating efficient knowledge transfer from a large model to a smaller one through Enhanced Cross-Attention layers.

Central to this research is the concept of LLM Modules, whereby a large, well-trained model functions as a knowledge repository, while a smaller model utilizes this knowledge to generate responses. Specifically, the authors utilize the Qwen2-1.5B model as the large, frozen knowledge source and the GPT-Neo-125M model as the smaller model structured to receive the representational knowledge via the Enhanced Cross-Attention layers. This approach diverges from traditional knowledge distillation strategies by directly transferring representations, thereby maintaining a more extensive set of information from the original model and effectively managing increased input sequence lengths, such as 128K tokens in Qwen2.

Methodological Advancements

The architecture is characterized by three principal components:

1. Knowledge Source: The Qwen2-1.5B model is employed to extract rich, pre-trained representations of input queries without fine-tuning its parameters.
2. Generation Module: The smaller GPT-Neo-125M model assimilates the external representations through Enhanced Cross-Attention layers to generate novel outputs.
3. Enhanced Cross-Attention: This innovative mechanism incorporates:
   • Linear Projections to align representation dimensions between the large and small models.
   • An Adapter Block for additional non-linear transformations.
   • A Gating Mechanism to blend original and external knowledge dynamically.

Experimental Findings

The experimental framework, grounded on the Bespoke-Stratos-17k dataset, underscores the efficiency of the CombinedModel—the integrated model comprising both Qwen2 and GPT-Neo. Through rigorous comparative analysis with other models like DeepSeek and variants of GPT-Neo, the paper illustrates that the CombinedModel is capable of achieving superior generation quality with reduced training requirements. Specifically, after 15 training epochs, significant reductions in both training loss (from 13.8 to 1.1) and validation loss were observed, underscoring the model's competency in converging efficiently.

Practical and Theoretical Implications

This research offers substantial implications for both practical applications and theoretical advancements within AI and NLP:

• Practical Efficiency: The modular approach, alongside the Enhanced Cross-Attention mechanism, significantly reduces computational costs traditionally associated with training large models, thereby enabling efficient knowledge transfer for specialized applications with limited resources.
• Theoretical Insights: By preserving a substantial portion of the original model's information, this approach promises more coherent and logically structured outputs compared to traditional knowledge distillation methods. This strategy presents a foundation for further exploration in integrating diverse architectures such as CNNs with LLMs, enhancing model adaptability across various domains.

Future Directions

The findings outlined in this paper open pathways for future investigations, notably in the field of expanding architectural versatility and refining model configurations. These avenues include experimenting with diverse model types and evaluating the impact of variant configurations of Cross-Attention layers on generation quality. Additionally, practical applications in specific business contexts could benefit from this approach, promoting adaptability and governance over AI-generated responses.

In conclusion, the proposed methodology represents a significant step toward overcoming computational barriers in LLM utilization, setting the stage for more economically feasible and adaptable AI model development. As the landscape of artificial intelligence continues to evolve, integrating such advanced knowledge transfer techniques holds promise for varied and nuanced applications in NLP, enriching both research and practical frontiers.