Improving Open-Ended Text Generation via Adaptive Decoding
Current language models decode text token by token according to probabilistic distribution, and determining the appropriate candidates for the next token is crucial to ensure generation quality. This study introduces adaptive decoding, a mechanism that empowers the language models to ascertain a sensible candidate set during the generation process dynamically. Specifically, we introduce an entropy-based metric called confidence and conceptualize determining the optimal candidate set as a confidence-increasing process. The rationality of including a token in the candidate set is assessed by leveraging the increment of confidence, enabling the model to determine the most suitable candidate set adaptively. The experimental results reveal that our method achieves higher MAUVE and diversity in story generation tasks and maintains certain coherence, underscoring its superiority over existing algorithms. The code is available at https://github.com/zwhong714/adaptive_decoding.
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