When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
Large language models (LLMs) deployed for structured generation (NER, JSON extraction, QA, and classification) lack formal reliability guarantees, and standard heuristic abstention policies violate user-specified risk targets on 7.5--12.5% of settings, with no per-deployment guarantee. We characterize when conformal risk control (CRC) can certify structured LLM outputs and when it provably cannot. First, we prove a sharpened, attained feasibility frontier: when base risk μexceeds target α, any distribution-free method must abstain on at least (μ-α)/(M-α) of inputs, yielding a closed-form feasibility test that decides whether CRC can work before running it. Second, we establish a proven ce...
| Authors | Varun Kotte |
|---|---|
| Primary category | cs.LG |
| Published | 27 June 2026 |
| Last updated at source | 7 September 2026 |
Silytics shows public metadata from arXiv and links to the original. Information may be incomplete or out of date. Last refreshed 17 October 2026. Licenses and terms of the original apply to the work itself. Not financial, legal or professional advice.