MixFlow Training: Alleviating Exposure Bias with Slowed Interpolation Mixture

The article likely discusses a novel training method, MixFlow, aimed at addressing exposure bias in language models. The core idea seems to involve a 'slowed interpolation mixture' which suggests a technique to control how the model integrates different data sources or training stages. The source being ArXiv indicates this is a research paper, likely detailing the method, its implementation, and experimental results. The focus on exposure bias suggests the work is relevant to improving the performance and robustness of large language models.