Title: Deconstructing Positional Information: From Attention Logits to Training Biases
Primary area: interpretability and explainable AI
Keywords: Position Encoding; Toeplitz Matrix; Attention Logit.

Abstract: Positional encodings, a mechanism for incorporating sequential information into the Transformer model, are central to contemporary research on neural architectures. Previous work has largely focused on understanding their function through the principle of distance attenuation, where proximity dictates influence. However, the interaction between positional and semantic information remains insufficiently explored, and the complexity of mainstream corpora hinders systematic, comparative studies of these methods. This paper addresses these challenges through a deconstruction of the attention-logit computation and a structured analysis of all mainstream positional encodings. A key focus is placed on Rotary Positional Embedding (RoPE), whose product-based structure uniquely facilitates a direct interaction between position and content. To probe this characteristic, we designed a novel synthetic task that explicitly demands a strong synthesis of positional and semantic information. As theoretically predicted, RoPE demonstrates a significant performance advantage over other encodings on this specialized task. Concurrently, this targeted evaluation uncovers an implicit training issue: a hidden bias manifesting as a distinct information aggregation phenomenon in the model's shallow layers, which we term the "single-head deposit pattern." Through subsequent ablation studies, we analyze this pattern and identify a method for its mitigation. These findings highlight the need for a deeper investigation into the training dynamics of positional encodings to bridge the gap between their theoretical design and practical implementation.

TL;DR: We propose a unifying perspective on the role of positional encoding and discover that rope training exhibits implicit biases.