FOR-Prompting: From Objection to Revision via an Asymmetric Prompting Protocol
This addresses the problem of enhancing self-revision in reasoning for AI systems, particularly benefiting small models and on-device use, though it is incremental as it builds on existing prompting methods.
The paper tackles the lack of external questioning in reasoning protocols like Chain of Thought by introducing FOR-Prompting, an asymmetric protocol that uses roles for proposing answers, raising objections, and enforcing consistency, resulting in a 22% point gain over single-prompt on GSM8K and improved accuracy for small models.
Reasoning protocols such as Chain of Thought (CoT) and Tree of Thought (ToT) organize internal deliberation but lack an explicit mechanism for external questioning that elicits self-revision. We present FOR-Prompting (From Objection to Revision Prompting), an asymmetric protocol where a Defender proposes an answer, an Objectioner raises question-style objections with no direct fixes, and a Host enforces consistency and closure. On GSM8K we observe about a 22% point gain over single-prompt and accuracy on par with CoT, with more than 10% higher ratings in reasoning and coherence from a uniform GPT 4.1 judge. FOR-Prompting also corrects mistakes without tools or human supervision on tricky queries, and improves performance for small-scale model (approx. 19% accuracy improved on Llama3.2:1b for GSM8K task), highlighting promise for small models and on personal device use. Beyond factual QA, qualitative analyses on open-ended tasks show enhanced exploration and refinement, with dialogue traces that make assumptions and trade-offs explicit. The protocol is model agnostic and operates purely at the prompt level through role-structured turns, so it works with hosted and local models of different sizes without retraining, and it supports large-scale study of objection-guided reasoning.