arXiv:2606.00251v1 Announce Type: new Abstract: The ability to recognize one's own limitations and decide whether to solve a problem or delegate is fundamental for reliable intelligent systems. Yet we show that modern large language models systematically lack this ability: across diverse model families and scales, they overestimate their competence and attempt queries they cannot solve. We refer to this ability as Capability Self-Assessment (CSA) and formulate it as a policy-learning problem, aiming to improve self-assessment while preserving the model's original capabilities. Our results show
The rapid advancement and deployment of large language models necessitate mechanisms for reliability and safety, making self-assessment a critical, emerging research front.
Teaching LLMs to understand their limitations is crucial for their effective and trustworthy integration into complex systems, preventing over-reliance and errors in critical applications.
The development of Capability Self-Assessment (CSA) moves LLMs from merely generating responses to exhibiting a rudimentary form of metacognition, enabling more robust AI design.
- · AI developers
- · AI safety researchers
- · AI-powered enterprise solutions
- · Unreliable AI applications
- · Developers ignoring AI ethics
LLMs will become more reliable and capable of identifying when they cannot solve a problem, reducing erroneous outputs.
Improved self-assessment will accelerate the deployment of autonomous AI agents by increasing trust and reducing human oversight requirements.
This could lead to a paradigm shift in human-AI collaboration, with AI systems proactively communicating their competence boundaries and delegation needs.
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Read at arXiv cs.AI