发现大模型处理序列时存在隔离与连续性难题，导致无法同时学习简单模式。
Continuity and Isolation Lead to Doubts or Dilemmas in Large Language Models
- 证明使用紧凑位置编码的Transformer必然出现隔离与连续性现象
- 实验验证理论局限在实际任务中确实存在，影响模式学习能力
- 适合关注模型内在机制与训练瓶颈的研究者
理解Transformer的工作原理及其信息处理机制，是推动其理论与实证发展的关键。本文揭示了Transformer中存在的两种现象：隔离与连续性。隔离指任何可学习的序列必须与其他可学习序列相隔离，因此某些序列无法被单个Transformer同时学习。连续性意味着围绕已学序列形成吸引子盆地，使得落入该盆地的任意序列都会坍缩至已学序列。我们数学上证明这些现象在所有采用紧凑位置编码的Transformer中均会涌现，并设计严谨实验，验证了所揭示的理论限制在实际尺度上确实发生。
原文摘要 · Abstract (English)
Understanding how Transformers work and how they process information is key to the theoretical and empirical advancement of these machines. In this work, we demonstrate the existence of two phenomena in Transformers, namely isolation and continuity. Both of these phenomena hinder Transformers to learn even simple pattern sequences. Isolation expresses that any learnable sequence must be isolated from another learnable sequence, and hence some sequences cannot be learned by a single Transformer at the same time. Continuity entails that an attractor basin forms around a learned sequence, such that any sequence falling in that basin will collapse towards the learned sequence. Here, we mathematically prove these phenomena emerge in all Transformers that use compact positional encoding, and design rigorous experiments, demonstrating that the theoretical limitations we shed light on occur on the practical scale.
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