## Understanding the Intentions of Language Models A recent study published on arXiv introduces a new approach to analyzing the internal decision-making process of language models (LLMs). The research focuses on the concept of "intention collapse," which is the compression of a complex internal state into a single token sequence during language generation. The researchers formalized this concept and defined three main metrics to quantify the intentions of the models: intention entropy (Hint), effective dimensionality (dimeff), and latent knowledge recoverability (Recov). These model-agnostic metrics aim to provide a deeper insight into how models reason before verbalizing their responses. ## Experiments and Preliminary Results The team conducted an experiment on a 4-bit Mistral 7B model, using 200 GSM8K problems. They compared a direct answer baseline with a "chain of thought" (CoT) regime and a "babble" control. The results showed that CoT increases accuracy from 5.5% to 53% and significantly reduces intention entropy. Despite producing fewer tokens than the "babble" control, CoT showed a higher global effective dimensionality. These preliminary results suggest that intention-level metrics can distinguish between different inference regimes and expose latent information that is partially lost during intention collapse. However, the study also highlights the limitations of current proxies, paving the way for further research in this field. ## The Future of Intention Research Understanding and measuring the intentions of language models is crucial for improving their reliability and transparency. This research represents a step forward towards developing more effective tools for analyzing and interpreting the behavior of artificial intelligence models.