Alternative Architectures for Multi-Token Prediction in LLMs | HackerNoon

The article discusses a novel architecture for multi-token prediction tasks, illustrating its scalability and performance benefits, especially with larger models. It examines various methodologies, including alternative architectures, their implications for training speed, and capabilities like global pattern learning. Various sections detail the experiments conducted, including both real and synthetic data, that assess induction capabilities and algorithmic reasoning. The article also speculates on why these architectures succeed, considering aspects like lookahead mechanisms and information-theoretic foundations. The conclusion emphasizes the architecture's implications in real-world applications while acknowledging its environmental impact and areas for future research.

The architecture described in Section 2 is not the only sensible option, but proved technically viable and well-performing in our experiments.

Replicating the unembedding matrix n times is a simple method for implementing multi-token prediction architectures, but it requires matrices that are prohibitive for large-scale trainings.

In another anticausal variant, the network starts by predicting the most distant tokens before gradually refining up to the following token, allowing both sequential and parallel architecture.

These architectures allow for a sequential forward/backward order, providing flexibility beyond the parallel architecture presented in Section 2.

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