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Thinking About Thinking: SAGE-nano's Inverse Reasoning for Self-Aware Language Models
- SAGE-nano introduces text-based inverse reasoning, allowing Large Language Models (LLMs) to explain their own reasoning chains post-hoc.
- The approach in SAGE-nano uses a metacognitive structure to reflect back via attention processes and generate explanations of reasoning choices.
- Through testing on logical reasoning puzzles, math problems, and ethical dilemmas, SAGE-nano shows high reasoning accuracy (74.6% on AQUA-RAT) and explanation quality (92.1% human preference score).
- The work on inverse reasoning aims to enhance interpretability and reasoning performance in AI systems, contributing to AI safety, education, and scientific discovery.
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