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：我们提出了一种名为GAMMT（基于多Transformer的生成式歧义模型）的新型序列数据模型，该模型基于概率集合构建。与传统模型不同，我们的方法承认序列的数据生成过程并非确定性的，而是具有歧义性，并受到一组概率的影响。为捕捉这种歧义性，GAMMT采用多个并联的Transformer，这些Transformer通过选择机制相互连接，从而实现对歧义概率的近似拟合。该方法的生成特性还支持输入令牌及序列的多重表示。尽管我们的模型尚未经过实验验证，但我们相信，该模型在建模具有不确定数据生成过程的序列时，具有实现高质量与高多样性的巨大潜力。