When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation

• URL: http://arxiv.org/abs/2606.29054v1
• Date: Sat, 27 Jun 2026 19:25:07 GMT
• Title: When Can Conformal Risk Control Certify LLM Outputs? Bounds, Impossibility, and Adaptation for Structured Generation
• Abstract summary: Large language models (LLMs) deployed for structured generation (NER, extraction, QA, and classification) lack formal reliability guarantees.<n>We characterize when conformal risk control (CRC) can certify structured LLM outputs and when it provably cannot.
• Score: 0.11280931253550518
• License: http://creativecommons.org/licenses/by/4.0/
• 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 (μ> α), any distribution-free method must abstain on at least ((μ-α)/(1-α)) examples, yielding a closed-form feasibility test: one can check whether CRC will work before running it. Second, we analyze a certification hierarchy across Hoeffding, empirical Bernstein, and a betting-based e-CRC bound, with strict gains in low-variance/large-sample regimes: the Hoeffding-to-Bernstein step delivers the largest gain (+37% certified configurations), while e-CRC adds value when calibration data is scarce (10% certification at 20% data versus 0% for Hoeffding). Third, we validate adaptive conformal inference (ACI) under cross-dataset shift, reducing risk-target violations from 71% to 21%, with residual failures concentrated exactly where the impossibility bound predicts. Across six open-weight models (3B--72B parameters), eight datasets, four tasks, and six nonconformity scores, hard NER/QA/CLS configurations are uncertifiable at (α= 0.10); relaxing to (α= 0.30--0.40) unlocks practical certification (47% NER, 40% QA, 60% CLS). The framework gives a three-step deployment recipe: check feasibility, select the bound and score, then mitigate shift.

Related papers

• Provable Limits and Certified Deferral for Verbalized Uncertainty in Small Language Models [0.0]
  Small open-weight language models increasingly run in private, offline, and cost-sensitive settings.<n>We study whether verbalized confidence can support risk-controlled deferral.
  arXiv  Detail & Related papers  (2026-08-05T17:11:04Z)
• Post-Hoc Trajectory-Risk Certification for Modular LLM-Based Security Agents [1.4915002678801434]
  We show that Bonferroni allocation is distribution-free but conservative under correlated errors.<n> coarse-to-fine label selection can create near-perfect measured correlation without learned dependence.<n>A modular certificate using per-stage certificates and a pairwise overlap bound yields a positive average gain of 0.6%.
  arXiv  Detail & Related papers  (2026-08-04T23:48:36Z)
• Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations [55.76952683833966]
  This paper proposes weighted CONCH (W-CONCH) and weighted CROC (W-CROC) to reduce confidence set size when data may be corrupted.<n>Experiments on image-based and real-world changepoint and root-cause benchmarks show that uncertainty-based weighting substantially reduces confidence set size.
  arXiv  Detail & Related papers  (2026-07-29T05:16:59Z)
• Traffic-Aware Randomized Smoothing for LLM-Based Network Intrusion Detection [1.4915002678801434]
  We present Traffic-Aware Randomized Smoothing (TA-RS) for intrusion detection systems.<n>It is a certified defense that injects Gaussian noise exclusively into the controllable (DC) subspace.<n>It achieves 55-100% certified accuracy on two datasets.
  arXiv  Detail & Related papers  (2026-07-15T13:08:19Z)
• Calibration Without Comprehension: Diagnosing the Limits of Fine-Tuning LLMs for Vulnerability Detection in Systems Software [1.0026496861838445]
  CWE-Trace is a framework for vulnerability detection built from 834 manually curated Linux kernel samples.<n>We evaluate eight vanilla LLMs and 15 LoRA fine-tuned variants across non-targeted detection, targeted detection, and CWE classification.
  arXiv  Detail & Related papers  (2026-06-18T17:19:26Z)
• CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery [51.07538881798502]
  Large language models (LLMs) offer a promising source of domain knowledge to complement statistical inference.<n>We propose CauTion, a framework that reliably integrates LLM domain knowledge into an ensemble of statistical causal discovery algorithms.<n>CauTion consistently outperforms both data-centric and LLM-augmented baselines.
  arXiv  Detail & Related papers  (2026-06-02T13:07:43Z)
• Distributional Energy-Based Models for Uncertainty-Aware Structured LLM Reasoning [40.342912574072024]
  Large Language Models produce structured outputs such as travel plans or code solutions.<n>Individual reasoning steps may appear correct while the output as a whole violates budgets, fails test cases, or contradicts earlier deductions.<n>We propose a learned quality scorer with deterministic analytical constraint penalties for verifying structured LLM outputs.
  arXiv  Detail & Related papers  (2026-05-15T17:08:27Z)
• The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation [1.8345614451086532]
  RLHF-aligned language models exhibit response homogenization on TruthfulQA.<n>40-79% of questions produce a single semantic cluster across 10 i.i.d. samples.
  arXiv  Detail & Related papers  (2026-03-25T09:35:15Z)
• Know When You're Wrong: Aligning Confidence with Correctness for LLM Error Detection [0.0]
  Large language models (LLMs) are increasingly deployed in critical decision-making systems.<n>We introduce a normalized confidence score based on output anchor token probabilities.<n>This enables direct detection of errors and hallucinations with minimal overhead.
  arXiv  Detail & Related papers  (2026-02-18T07:05:12Z)
• LEC: Linear Expectation Constraints for False-Discovery Control in Selective Prediction and Routing Systems [95.35293543918762]
  Large language models (LLMs) often generate unreliable answers, while uncertainty methods fail to fully distinguish correct from incorrect predictions.<n>We address this issue through the lens of false discovery rate (FDR) control, ensuring that among all accepted predictions, the proportion of errors does not exceed a target risk level.<n>We propose LEC, which reinterprets selective prediction as a constrained decision problem by enforcing a Linear Expectation Constraint.
  arXiv  Detail & Related papers  (2025-12-01T11:27:09Z)
• Conformal Lesion Segmentation for 3D Medical Images [82.92159832699583]
  We propose a risk-constrained framework that calibrates data-driven thresholds via conformalization to ensure the test-time FNR remains below a target tolerance.<n>We validate the statistical soundness and predictive performance of CLS on six 3D-LS datasets across five backbone models, and conclude with actionable insights for deploying risk-aware segmentation in clinical practice.
  arXiv  Detail & Related papers  (2025-10-19T08:21:00Z)
• Unsupervised Conformal Inference: Bootstrapping and Alignment to Control LLM Uncertainty [49.19257648205146]
  We propose an unsupervised conformal inference framework for generation.<n>Our gates achieve close-to-nominal coverage and provide tighter, more stable thresholds than split UCP.<n>The result is a label-free, API-compatible gate for test-time filtering.
  arXiv  Detail & Related papers  (2025-09-26T23:40:47Z)
• Conformal Prediction for Privacy-Preserving Machine Learning [83.88591755871734]
  Using AES-encrypted variants of the MNIST dataset, we demonstrate that Conformal Prediction methods remain effective even when applied directly in the encrypted domain.<n>Our work sets a foundation for principled uncertainty quantification in secure, privacy-aware learning systems.
  arXiv  Detail & Related papers  (2025-07-13T15:29:14Z)
• COIN: Uncertainty-Guarding Selective Question Answering for Foundation Models with Provable Risk Guarantees [51.5976496056012]
  COIN is an uncertainty-guarding selection framework that calibrates statistically valid thresholds to filter a single generated answer per question.<n>COIN estimates the empirical error rate on a calibration set and applies confidence interval methods to establish a high-probability upper bound on the true error rate.<n>We demonstrate COIN's robustness in risk control, strong test-time power in retaining admissible answers, and predictive efficiency under limited calibration data.
  arXiv  Detail & Related papers  (2025-06-25T07:04:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.