When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
arXiv:2606.29054v1 Announce Type: new Abstract: Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies miss user-specified risk targets by 7.5--12.5%. We characterize when conformal risk control (CRC) can certify structured LLM outputs and when it provably cannot. First, we prove an impossibility result: when the base risk (\mu > \alpha), any distribution-free method must abstain on at least ((\mu-\alpha)/(1-\alpha)) examples, yielding a closed-form feasibility
The paper addresses a critical, current need for reliability guarantees in large language models, especially as they move into high-stakes structured generation tasks, where their current heuristic performance is insufficient.
This research provides a foundational understanding of LLM reliability for structured outputs, identifying both the possibilities and inherent limitations of certifying their performance, directly impacting deployability and trust.
The ability to formally certify LLM outputs for structured generation tasks promises to enable broader and safer deployment of AI in critical applications that currently rely on imprecise heuristic controls.
- · AI Safety Researchers
- · Enterprises deploying LLMs for structured tasks
- · Developers of formal verification methods for AI
- · SaaS companies integrating LLM capabilities
- · Companies relying solely on heuristic LLM validation
- · LLM developers ignoring formal reliability
- · Applications requiring 100% LLM accuracy without certification options
Increased trust and adoption of LLMs for specific, high-value structured data tasks like legal document analysis or medical information extraction.
Development of new tooling and platforms specifically designed to incorporate and manage conformal risk control in LLM deployments.
Potential for regulatory bodies to adopt or mandate formal certification standards for AI systems in sensitive sectors, based on principles like conformal risk control.
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Read at arXiv cs.LG