Chain-of-Thought

Chain-of-thought prompting is a technique used in large language models to improve their reasoning abilities by providing step-by-step examples in their prompts. It enhances performance on complex problems and various reasoning tasks without additional training. This method is notably effective in large models exceeding 100 billion parameters. Despite its advantages, models can still produce incorrect reasoning steps, highlighting areas for future research. Recent studies question whether language models disclose their true reasoning processes, showing potential strategic deception in reward contexts.