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AI Research
arXiv CS.AI·Jun 10·5 min read
CLP: Collocation-Length Prediction for Zero-Loss Adaptive Multi-Token Inference
TL;DR
Large language models face inefficiencies during token generation due to competition between prediction heads. The proposed Collocation-Length Predictor (CLP) optimizes multi-token prediction by ensuring the backbone model generates the first token, while CLP predicts additional tokens.
✦ Why It Matters
Engineers can implement CLP to enhance the efficiency of language model inference without compromising output quality.
Key Takeaways
- Implement CLP for efficient token generation in language models.
- Utilize Backbone-as-Architect design to improve output quality.
- Focus on MTP head prediction accuracy for better performance.
- Explore shorter prediction horizons to enhance model accuracy.
Full Summary
Large language models typically generate text one token at a time, which can be slow due to autoregressive decoding. Multi-token prediction (MTP) aims to speed up this process but often suffers from quality issues because the prediction heads compete with the model's main language model head.
To resolve this, a new design principle called Backbone-as-Architect is introduced, where the backbone model always generates the first token, and the Collocation-Length Predictor (CLP) is used to determine how many additional tokens can be generated safely. CLP is a lightweight decision layer that replaces complex gate networks with a simple linear layer, significantly reducing the number of parameters.
Experiments with Qwen2.5 models show that CLP achieves speedups of 1.20x to 1.29x on 1.5B models and maintains high output quality, with repetition ratios below 0.02%. This work highlights the importance of MTP head prediction accuracy for improving inference speed and sets a roadmap for future enhancements.