让语言模型生成多个词为原子单位，提升质量和速度
Generation with Dynamic Vocabulary
- 用任意文本片段作为生成单元，替代传统单个词
- 质量提升25%（MAUVE），延迟降低20%
- 无需训练即可适配多领域，适合问答中可靠引文生成
我们提出一种新的动态词汇表，可在生成过程中引入任意文本片段，这些片段作为基本生成单元，类似于传统静态词汇表中的词元。实验表明，能够原子化生成多词显著提升生成质量与效率：相比标准语言模型，MAUVE指标提升25%，延迟降低20%。该动态词汇表可即插即用，适用于多种下游任务。例如，它能在无需训练的情况下应用于不同领域；在问答任务中生成更可靠的引用，显著提升引用效果，且不损害答案准确性。
原文摘要 · Abstract (English)
We introduce a new dynamic vocabulary for language models. It can involve arbitrary text spans during generation. These text spans act as basic generation bricks, akin to tokens in the traditional static vocabularies. We show that, the ability to generate multi-tokens atomically improve both generation quality and efficiency (compared to the standard language model, the MAUVE metric is increased by 25%, the latency is decreased by 20%). The dynamic vocabulary can be deployed in a plug-and-play way, thus is attractive for various downstream applications. For example, we demonstrate that dynamic vocabulary can be applied to different domains in a training-free manner. It also helps to generate reliable citations in question answering tasks (substantially enhancing citation results without compromising answer accuracy).
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