When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
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Abstract (arXiv)
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 μ exceeds 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 certification phase diagram across Hoeffding, empirical Bernstein, and a betting-based e-CRC bound, verified over 716 configurations (six open-weight models 3B--72B, eight datasets, six scores): at strict targets (α <= 0.20) the certified sets are nested (51/72/80 certified; Hoeffding-to-Bernstein the largest upgrade, +41%; e-CRC best under calibration scarcity), while at relaxed targets the Hoeffding-Bernstein ordering reverses at a closed-form frontier. Third, we show a negative shift result with a constructive counterpart: under cross-dataset shift the target is violated on 14 of 16 transfers by static CRC and every tested adaptive-conformal-inference (ACI) step size, yet a full-feedback anytime-valid monitor certifies 0 of 16 while remaining non-vacuous. Relaxing the target to α = 0.40 unlocks practical certification (28% NER, 13% QA, 19% CLS). The framework gives a three-step deployment recipe: check feasibility, select the bound and score, then re-check under shift.
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 41 of its 56 references that are in the map. First posted on arXiv 27 Jun 2026.
Builds on
41 of 56 references.
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29 more
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Citing papers per year: 2026: 4.
4 papers.
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- 2026Uncertainty Quantification for LLM Agents: A Taxonomy, an Evaluation Protocol, and an Empirical Study7 Sep 2026
- 2026The Exceedance Design Effect: Effective Sample Size for Thresholds under Clustering21 Aug 2026
- 2026Non-Degenerate Risk Certification for Automated Security Decisions: A Decision-Contract Theory with ATT&CK-Aligned Triage as a Worked Instance12 Aug 2026