We introduce a novel model called GAMMT (Generative Ambiguity Models using Multiple Transformers) for sequential data that is based on sets of probabilities. Unlike conventional models, our approach acknowledges that the data generation process of a sequence is not deterministic, but rather ambiguous and influenced by a set of probabilities. To capture this ambiguity, GAMMT employs multiple parallel transformers that are linked by a selection mechanism, allowing for the approximation of ambiguous probabilities. The generative nature of our approach also enables multiple representations of input tokens and sequences. While our models have not yet undergone experimental validation, we believe that our model has great potential to achieve high quality and diversity in modeling sequences with uncertain data generation processes.

翻译：我们介绍了一种新型的基于概率集的序列数据模型，称为 GAMMT （利用多个变换器生成多义模型），它与传统模型不同，因为我们的方法认识到一个序列的数据生成过程并非是确定的，而是存在歧义并受到一组概率的影响。为了捕捉这种不确定性，GAMMT 采用多个并行的变换器，通过选择机制相互链接，以近似不确定概率。我们方法的生成性质还允许输入标记和序列的多重表示。虽然我们的模型尚未经过实验验证，但我们相信我们的模型具有在对具有不确定数据生成过程的序列进行建模方面实现高质量和多样性的巨大潜力。