Transformers rely on pattern recognition and language-based reasoning.
Thus, reasoning tokens serve as a mechanism for token-based logical progression, allowing models like ChatGPT to simulate math insights by leveraging pattern recognition, token relationships, and sequential reasoning, even without explicit symbolic or mathematical processing built into the model itself.
Mathematical Reasoning in Transformers
In the context of models like ChatGPT, reasoning tokens refer to the individual pieces of language that contribute to the step-by-step logical process used by the model to solve problems, including mathematical ones.
5. Logical Continuity and Error Correction
- Reasoning tokens enable the model to maintain logical continuity, allowing it to backtrack or adjust outputs based on the sequence of previously generated tokens. For example, if the model makes a mistake in an earlier step (like a miscalculation), it can revise its response as it generates subsequent tokens that recognize the inconsistency.