NDP: Next Distribution Prediction as a More Broad Target
Abstract: Large language models (LLMs) trained on next-token prediction (NTP) paradigm have demonstrated powerful capabilities.
However, the existing NTP paradigm contains several limitations, particularly related to planned task complications and error propagation during inference.
In our work, we extend the critique of NTP, highlighting its limitation also due to training with a narrow objective: the prediction of a sub-optimal one-hot distribution.
Based on this insight, we introduce Next Distribution Prediction (NDP), which uses statistical distributions to replace the one-hot targets, enhancing learning without extra online training time.
We conducted experiments across translation, general task, language transfer, and medical domain adaptation.
Compared to NTP, NDP can achieve up to +2.97 COMET improvement in translation tasks, +0.61 average improvement in general tasks, and incredible +10.75 average improvement in the vertical domain (Medical here). This demonstrates the concrete benefits of addressing the target narrowing problem, pointing to a new direction for future work on improving NTP.
Paper Type: Long
Research Area: NLP Applications
Research Area Keywords: Next Token Prediciton, Large Language Model, Language Modeling
Contribution Types: Theory
Languages Studied: English, German, Chinese
Submission Number: 2228
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