Unveiling 'Intention Collapse': A Novel Approach to Understanding Reasoning in Language Models

This paper introduces a novel concept, 'intention collapse,' and proposes metrics to quantify the information loss during language generation. The initial experiments, while small-scale, offer a promising direction for analyzing the internal reasoning processes of language models, potentially leading to improved model interpretability and performance. However, the limited scope of the experiment and the model-agnostic nature of the metrics require further validation across diverse models and tasks.