The quest for models that can mimic human-like reasoning is explored in the paper Quiet-STaR: Language Models Can Teach Themselves to Think Before Speaking. Authors present a system where language models self-learn to produce internal dialogues, which serve as rationales for text predictions. It’s a step towards more thoughtful and reflective AI.

• Quiet-STaR is an extension of the Self-Taught Reasoner (STaR), generating internal thoughts for better predictions.
• It solves computational costs by parallel token generation, learnable thought markers, and advanced teacher-forcing methods.
• Rationales particularly improve predictions for challenging tokens, leading to notable progress in zero-shot task performance.

The success of Quiet-STaR highlights a significant move towards language models that can autonomously learn reasoning in a diverse array of contexts, without direct fine-tuning. It also points out the latitude for language models to transform into entities possessing a form of ‘thoughtfulness,’ further bridging the gap between AI and human cognitive processes.