Better & Faster Large Language Models via Multi-token Prediction
Tags: machinelearning, ai, beginners, datascience
Published: 2024-05-02 12:06:02

*This is a Plain English Papers summary of a research paper called [Better & Faster Large Language Models via Multi-token Prediction](https://aimodels.fyi/papers/arxiv/better-faster-large-language-models-via-multi). If you like these kinds of analysis, you should subscribe to the [AImodels.fyi newsletter](https://aimodels.substack.com) or follow me on [Twitter](https://twitter.com/mikeyoung44).*

## Overview

- Large language models like GPT and Llama are typically trained to predict the next token in a sequence.
- This paper suggests that training models to predict multiple future tokens at once can lead to higher sample efficiency and improved downstream capabilities.
- The method involves using multiple independent output heads to predict the next n tokens, operating on a shared model trunk.
- This multi-token prediction task can be used as an auxiliary training objective, with benefits for both code and natural language models.

## Plain English Explanation

The paper explores an alternative approach to training large language models like [GPT](https://aimodels.fyi/papers/arxiv/is-next-token-prediction-sufficient-gpt-exploration) and [Llama](https://aimodels.fyi/papers/arxiv/toward-theory-tokenization-llms). Typically, these models are trained to predict the next single token in a sequence, using a "next-token prediction" loss.

However, the researchers propose that training the models to predict multiple future tokens at once can be more effective. Specifically, at each position in the training data, the model is asked to predict the following n tokens using n independent output heads, all built on top of a shared model trunk.

Treating this multi-token prediction as an auxiliary training task, the researchers found that it led to improved downstream capabilities for both code and natural language models, without any increase in training time. The benefits were especially pronounced for generative tasks, like coding, where the multi-token models outperformed strong baselines by several percentage points.

The researchers also found that the multi-token models were up to 3 times faster at inference, even with large batch sizes. This is likely because predicting multiple tokens at once reduces the number of sequential predictions required.

Overall, the key idea is that training language models to look ahead and predict multiple future tokens, rather than just the next one, can lead to significant performance gains across a range of applications.

## Technical Explanation

The paper explores an alternative training approach for large language models, where the model is asked to predict multiple future tokens at each position in the training corpus, rather than just the next token.

Specifically, the researchers introduce a "multi-token prediction" objective, where the model uses n independent output heads to predict the next n tokens, all built on top of a shared model trunk. This is treated as an auxiliary training task, in addition to the standard next-token prediction loss.

The researchers evaluate this approach on both code and natural language models, and find consistent improvements in downstream capabilities, with no increase in training time. The gains are especially pronounced on generative benchmarks like coding, where the multi-token models outperform strong baselines by several percentage points.

For example, the researchers' 13B parameter models solve 12% more problems on the HumanEval benchmark and 17% more on the MBPP benchmark, compared to similar-sized next-token models.

The researchers also observe that the multi-token models are significantly faster at inference, up to 3 times faster, even with large batch sizes. This is likely due to the fact that predicting multiple tokens at once reduces the number of sequential predictions required.

Experiments on small algorithmic tasks further demonstrate that the multi-token prediction objective is favorable for the development of induction heads and algorithmic reasoning capabilities.

Overall, the key insight is that training language models to [think before they speak](https://aimodels.fyi/papers/arxiv/think-before-you-speak-training-language-models), by predicting multiple future tokens instead of just the next one, can lead to substantial performance improvements across a range of applications.

## Critical Analysis

The paper presents a compelling approach to training large language models, with clear empirical benefits demonstrated across a range of tasks and benchmarks. The multi-token prediction objective seems to be a simple yet effective way to improve sample efficiency and downstream capabilities, without increasing training time.

However, the paper does not delve into the potential limitations or drawbacks of this approach. For example, it's unclear how the multi-token predictions are used during inference, and whether there are any trade-offs in terms of model complexity or [perplexity](https://aimodels.fyi/papers/arxiv/can-perplexity-predict-fine-tuning-performance-investigation).

Additionally, the paper focuses primarily on performance metrics, without much discussion of the underlying mechanisms or cognitive capabilities that the multi-token prediction objective might be encouraging. It would be interesting to see further analysis on how this approach affects the model's ability to [perform multi-word tokenization](https://aimodels.fyi/papers/arxiv/multi-word-tokenization-sequence-compression) or engage in more sophisticated forms of [algorithmic reasoning](https://aimodels.fyi/papers/arxiv/toward-theory-tokenization-llms).

Overall, the research presented in this paper is a valuable contribution to the field of large language model training, but further exploration of the approach's limitations and potential implications would be a useful next step.

## Conclusion

This paper proposes an innovative training approach for large language models, where the models are trained to predict multiple future tokens at each position in the training corpus, rather than just the next token. The researchers demonstrate that this "multi-token prediction" objective leads to improved sample efficiency and downstream capabilities, with particularly strong gains on generative tasks like coding.

The key insight is that by training models to "think before they speak" and anticipate multiple future tokens, they can develop more sophisticated language understanding and generation abilities. This approach seems to be especially beneficial for larger model sizes and holds up well when training for multiple epochs.

The findings presented in this paper have significant implications for the development of more capable and efficient large language models, which are increasingly important for a wide range of applications in natural language processing and beyond. As the field continues to progress, it will be exciting to see how this and other novel training techniques can push the boundaries of what these models are able to achieve.

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