Attention's Gravitational Field:A Power-Law Interpretation of Positional Correlation
This work addresses model optimization and interpretability for LLM researchers, representing a significant step in interpreting the Attention mechanism.
The paper tackles the problem of positional relationships and encodings in Large Language Models by introducing the Attention Gravitational Field concept, which decouples positional encodings from semantic embeddings to achieve superior accuracy compared to existing methods.
This paper explores the underlying principles of positional relationships and encodings within Large Language Models (LLMs) and introduces the concept of the Attention Gravitational Field (AGF). By decoupling positional encodings from semantic embeddings, we optimize the model architecture and achieve superior accuracy compared to prevailing encoding methods. Furthermore, we provide an in-depth analysis of AGF, demonstrating its intrinsic consistency with learning and stability curves, as well as its empirical alignment with Newton's Law of Universal Gravitation. By offering a rigorous theoretical exploration of these phenomena, this work represents a significant step toward interpreting the Attention mechanism and unlocks new possibilities for future research in model optimization and interpretability.