研究人员发表了一篇论文，详细介绍了均场Transformer中的浓度现象，特别是在推理的低温环境下分析其行为。该研究使用均场连续性方程来模拟token演化，并证明了token分布在Transformer矩阵诱导的投影映射下会迅速集中。这种集中在适度时间内保持亚稳态，其Wasserstein距离与温度和推理时间相关。 AI
影响 为Transformer行为提供了理论见解，可能为未来的模型设计和优化提供信息。
排序理由 该集群包含一篇详细介绍Transformer模型行为的理论分析和数值实验的学术论文。
在 Hugging Face Daily Papers 阅读 →
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Transformers with self-attention modules as their core components have become an integral architecture in modern large language and foundation models. In this paper, we study the evolution of tokens in deep encoder-only transformers at inference time which is described in the lar…
Transformers with self-attention modules as their core components have become an integral architecture in modern large language and foundation models. In this paper, we study the evolution of tokens in deep encoder-only transformers at inference time which is described in the lar…