The pitfalls of next-token prediction
<|reference_start|>The pitfalls of next-token prediction: Can a mere next-token predictor faithfully model human intelligence? We crystallize this emerging concern and correct popular misconceptions surrounding it, and advocate a simple multi-token objective.   As a starting point, we argue that the two often-conflated phases of next-token prediction -- autoregressive inference and teacher-forced training -- must be treated distinctly. The popular criticism that errors can compound during autoregressive inference, crucially assumes that teacher-forcing has learned an accurate next-token predictor. This assumption sidesteps a more deep-rooted problem we expose: in certain classes of tasks, teacher-forcing can simply fail to learn an accurate next-token predictor in the first place. We describe a general mechanism of how teacher-forcing can fail, and design a minimal planning task where both the Transformer and the Mamba architecture empirically fail in that manner -- remarkably, despite the task being straightforward to learn.   Finally, we provide preliminary evidence that this failure can be resolved using a simple modification that predicts multiple tokens in advance. We hope this finding can ground future debates and inspire explorations beyond the next-token prediction paradigm. We make our code available under https://github.com/gregorbachmann/Next-Token-Failures<|reference_end|>
@article{bachmann2024the,
    title={The pitfalls of next-token prediction},
    author={Gregor Bachmann, Vaishnavh Nagarajan},
    journal={arXiv preprint arXiv:2403.06963},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.06963},
    primaryClass={cs.CL cs.AI cs.LG}
}