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CoVeR: Conformal Calibration for Versatile and Reliable Autoregressive Next-Token Prediction

arXiv 2509.04733 ↗First posted 5 Sep 2025cs.LG cs.AIcited by 0

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Abstract (arXiv)

Autoregressive pre-trained models combined with decoding methods have achieved impressive performance on complex reasoning tasks. While mainstream decoding strategies such as beam search can generate plausible candidate sets, they often lack provable coverage guarantees, and struggle to effectively balance search efficiency with the need for versatile trajectories, particularly those involving long-tail sequences that are essential in certain real-world applications. To address these limitations, we propose textscCoVeR, a novel model-free decoding strategy wihtin the conformal prediction framework that simultaneously maintains a compact search space and ensures high coverage probability over desirable trajectories. Theoretically, we establish a PAC-style generalization bound, guaranteeing that textscCoVeR asymptotically achieves a coverage rate of at least 1 - α for any target level α ∈ (0,1).

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 26 of its 31 references that are in the map. First posted on arXiv 5 Sep 2025.

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References · 26

  1. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  2. 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,146
  3. 2022Self-Instruct: Aligning Language Models with Self-Generated Instructionscited by 2,674
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  10. 2022Calibrating Sequence likelihood Improves Conditional Language Generationcited by 121
  11. 2023Class-Conditional Conformal Prediction with Many Classescited by 109
  12. 2023A Survey of Reasoning with Foundation Modelscited by 70
14 more
  1. 2019PAC Confidence Sets for Deep Neural Networks via Calibrated Predictioncited by 59
  2. 2025Large Language Models Pass the Turing Testcited by 48
  3. 2023Conformal Nucleus Samplingcited by 32
  4. 2023Say What You Mean! Large Language Models Speak Too Positively about Negative Commonsense Knowledgecited by 32
  5. 2020RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axiomscited by 28
  6. 2024Deductive Beam Search: Decoding Deducible Rationale for Chain-of-Thought Reasoningcited by 24
  7. 2024Probabilistic Conformal Prediction with Approximate Conditional Validitycited by 23
  8. 2021Relating Neural Text Degeneration to Exposure Biascited by 19
  9. 2023Conformal Autoregressive Generation: Beam Search with Coverage Guaranteescited by 16
  10. 2022Improved Beam Search for Hallucination Mitigation in Abstractive Summarizationcited by 10
  11. 2025Efficient Beam Search for Large Language Models Using Trie-Based Decodingcited by 10
  12. 2022UnCommonSense: Informative Negative Knowledge about Everyday Conceptscited by 8
  13. 2023Less Likely Brainstorming: Using Language Models to Generate Alternative Hypothesescited by 8
  14. 2024Language-Informed Beam Search Decoding for Multilingual Machine Translationcited by 2

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