arXiv:2606.14961v1 Announce Type: new Abstract: Chain-of-thought (CoT) reasoning can improve LLM performance, but high answer confidence may be misleading when the accompanying CoT rationale is plausible yet incomplete or poorly supported. We study confidence--rationale alignment: whether a model's confidence in its committed answer is justified by its generated rationale. We introduce a GRPO-based reinforcement learning framework that jointly rewards answer correctness, committed-answer probability, and rubric-based rationale support, where the rubric assesses grounding, coherence, task match
The proliferation of advanced LLMs and their integration into critical applications drives the immediate need for robust and reliable reasoning, making confidence-rationale alignment a timely research area.
This research addresses a fundamental limitation in current AI reasoning by improving the trustworthiness and interpretability of LLM outputs, which is crucial for their reliable deployment in sensitive domains.
The ability to ensure a model's confidence is justified by its rationale marks a step towards more transparent and auditable AI systems, potentially accelerating the adoption of LLMs in high-stakes environments.
- · AI safety researchers
- · Developers of AI agents
- · Enterprises deploying LLMs
- · Users of complex AI systems
- · Systems relying on uninterpretable black-box AI
- · Companies offering limited AI explainability tools
Improved reliability and explainability of Chain-of-Thought reasoning in Large Language Models.
Increased trust and faster adoption of AI agents in workflow automation and decision-making.
New regulatory frameworks for AI that incorporate requirements for confidence-rationale alignment.
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Read at arXiv cs.CL