arXiv:2607.01612v1 Announce Type: new Abstract: Training large language models (LLMs) with reinforcement learning (RL) has significantly advanced their performance on reasoning and question-answering tasks. However, prevailing RL reward designs typically prioritize response correctness, neglecting to incentivize models to express their confidence accurately. This leads to a critical problem: performance gains are often accompanied by poor calibration between confidence and accuracy, misleading models to overconfidently hallucinate when uncertain. To address this limitation, we propose $\textbf
As LLMs advance in reasoning tasks, the need to address their tendency to overconfidently hallucinate becomes more urgent for real-world reliability and adoption.
Accurate confidence calibration is crucial for deploying LLMs in sensitive applications where unchecked AI 'hallucinations' can lead to significant errors and distrust.
The focus expands from merely improving LLM performance to enhancing their reliability and trustworthiness by addressing the critical issue of miscalibrated confidence.
- · LLM developers
- · AI safety researchers
- · Enterprises adopting AI
- · AI-powered decision systems
- · Unreliable LLMs
- · Applications highly sensitive to AI 'hallucinations'
- · Unscrupulous AI deployments
Further research and development into confidence calibration mechanisms for large language models will accelerate.
Increased trust in LLM outputs could lead to broader integration of AI in high-stakes environments like finance and healthcare.
The development of robust, calibrated LLMs might accelerate the adoption of autonomous AI agents in complex decision-making roles.
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Read at arXiv cs.AI