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About this paper
Yuchang Sun, Yanxi Chen, Yaliang Li, Bolin Ding
Researchers from Alibaba Group introduce latent tokens, a lightweight, parameter-efficient method to enhance Transformer-based Large Language Models' latent computational capabilities. The approach integrates non-interpretable, learnable token embeddings to improve performance and out-of-distribution generalization in complex tasks like long-form generation, information retrieval, and instruction following.
Key insights
- Latent tokens can act as continuous self-prompts, guiding the model to maintain coherence and follow rules over long, consistent generations, particularly in OOD scenarios.
- They function as internal memory aids or "anchors," helping the model retrieve and process relevant information from extended input contexts.
- Function specialization (assigning different latent token groups to specific roles) allows for multi-faceted internal state management, significantly improving instruction adherence in complex OOD tasks.
Summary by alphaXiv.
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