AI BriefingHorizontal Scaling Technique for LLMs Without Weight Modification
Key point
A Residual Coupling technique is proposed that connects fixed language models via linear bridges to boost performance without modifying weights.
Details
Residual Coupling (RC) is a method that connects language models with frozen weights in parallel, injecting hidden states through small trainable linear bridges.
Since this structure does not modify the model's weights at all, it prevents catastrophic forgetting, and by restricting the bridges to only map the geometric relationships of the existing representation space, it prevents overfitting.
Compared to the MoE (Mixture-of-Experts) approach, the key performance metrics are as follows:
- Medical: Reduced Perplexity from 56.80 to 11.02, a decrease of approximately 80.7%.
- TruthfulQA Health: Improved accuracy by 9.1%p over the baseline.
- Coding tests: Resolved the mismatch issue between models with different tokenizers, lowering Perplexity to 5.91.
This framework moves away from the vertical scaling approach of building a single massive model, presenting a new paradigm of horizontal scaling for multi-model systems.
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