Paper Reading: Beyond Next-Token Prediction: Enhancing Language Models with Multi-Token Outputs

Session led by: Lucia Mocz

In this talk, we will look at groundbreaking research on advancing the training of large language models. Traditional models like GPT and Llama rely on next-token prediction, which can be expensive and time-consuming, but this research suggests a more efficient approach: multi-token prediction.

Key Highlights:

• Enhanced Sample Efficiency: Training models to predict multiple future tokens at once increases sample efficiency, leading to significant improvements in downstream capabilities.
• Innovative Architecture: By employing n independent output heads on a shared model trunk, better performance with no additional training time can be achieved.
• Superior Performance: 13B parameter models outperform strong baselines on generative benchmarks, solving 12% more problems on HumanEval and 17% more on MBPP.
• Algorithmic Reasoning: Multi-token prediction fosters the development of induction heads and enhances algorithmic reasoning capabilities.
• Faster Inference: Models trained with 4-token prediction are up to 3 times faster during inference, even with large batch sizes.

Join us to explore how multi-token prediction can revolutionize language model training and performance, especially for coding and natural language tasks.

Don't miss this opportunity to learn about the future of language model training and its practical applications!

Paper: https://arxiv.org/pdf/2404.19737