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arxiv: 2604.13991 · v1 · submitted 2026-04-15 · 💻 cs.CL · cs.AI· cs.LG

Adaptive Conformal Prediction for Improving Factuality of Generations by Large Language Models

Aleksandr Rubashevskii , Dzianis Piatrashyn , Preslav Nakov , Maxim Panov This is my paper

Pith reviewed 2026-05-10 13:13 UTC · model grok-4.3

classification 💻 cs.CL cs.AIcs.LG
keywords adaptive conformal predictionlarge language modelsfactualityconditional coverageselective predictionuncertainty quantificationLLM calibrationprompt adaptation
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The pith

Adaptive conformal prediction can be extended to LLMs to deliver prompt-specific factuality guarantees while preserving marginal coverage.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper introduces a way to adapt conformal prediction scores to the specific prompt given to a large language model. Standard conformal methods use a single calibration set and threshold for all inputs, which can produce too many or too few reliable outputs depending on the task or wording. By transforming scores in a prompt-dependent manner, the method aims to improve how closely the actual coverage matches the desired level for each input group. This matters because it gives a statistically valid way to identify and filter unreliable generations without discarding useful ones across varied prompts.

Core claim

We propose an adaptive conformal prediction approach that extends conformal score transformation methods to LLMs, with applications to long-form generation and multiple-choice question answering. This enables prompt-dependent calibration, retaining marginal coverage guarantees while improving conditional coverage. In addition, the approach naturally supports selective prediction, allowing unreliable claims or answer choices to be filtered out in downstream applications. We evaluate our approach on multiple white-box models across diverse domains and show that it significantly outperforms existing baselines in terms of conditional coverage.

What carries the argument

Prompt-adaptive conformal score transformations that adjust the nonconformity scores or thresholds according to input features while preserving the marginal coverage property of standard conformal prediction.

If this is right

  • The method supports selective prediction by discarding generations whose conformal scores fall outside the calibrated interval.
  • It applies directly to both long-form text generation and multiple-choice question answering tasks.
  • It yields higher conditional coverage than non-adaptive baselines on white-box models tested across multiple domains.
  • The same adaptive transformation framework can be reused for other downstream filtering decisions without retraining the underlying LLM.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar prompt-dependent adjustments could be tested on black-box models if surrogate scores or external verifiers are available.
  • The technique might reduce wasted computation in production pipelines by avoiding over-filtering on easy prompts.
  • One could examine whether the adaptation function itself can be learned from unlabeled data while still guaranteeing coverage.

Load-bearing premise

The adaptation rule can be chosen so that it improves coverage for specific prompts without violating the overall marginal coverage guarantee that holds across the entire distribution.

What would settle it

An evaluation on a new set of prompts where the adaptive method's empirical coverage within prompt groups deviates substantially from the target level or where the overall coverage across all prompts falls below the promised marginal guarantee.

Figures

Figures reproduced from arXiv: 2604.13991 by Aleksandr Rubashevskii, Dzianis Piatrashyn, Maxim Panov, Preslav Nakov.

Figure 1
Figure 1. Figure 1: Long-form QA target vs. empirical coverage for (a) Conformal Factuality and (b) [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Multi-choice QA experimental results: target vs. empirical coverage for (a) [PITH_FULL_IMAGE:figures/full_fig_p006_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Dolan-More profiles for calibration error for (a) Mistral 7B, (b) Llama3 8B. Problems ´ are defined by (category, seed, α) with α ∈ {0.5, 0.55, . . . , 0.8}, 20 seeds and 16 categories. Calibration error is defined as |empirical coverage − (1 − α)|, normalized per problem. The x-axis (δ) is plotted on a logarithmic scale. Curves show the fraction of problems within a factor δ of the best (higher is better)… view at source ↗
Figure 4
Figure 4. Figure 4: Multi-choice QA target vs. empirical coverage for (a) Conformal Factuality and (b) [PITH_FULL_IMAGE:figures/full_fig_p015_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: T-SNE visualization of PCA clustering of long-form QA prompts. [PITH_FULL_IMAGE:figures/full_fig_p016_5.png] view at source ↗
read the original abstract

Large language models (LLMs) are prone to generating factually incorrect outputs. Recent work has applied conformal prediction to provide uncertainty estimates and statistical guarantees for the factuality of LLM generations. However, existing approaches are typically not prompt-adaptive, limiting their ability to capture input-dependent variability. As a result, they may filter out too few items (leading to over-coverage) or too many (under-coverage) for a given task or prompt. We propose an adaptive conformal prediction approach that extends conformal score transformation methods to LLMs, with applications to long-form generation and multiple-choice question answering. This enables prompt-dependent calibration, retaining marginal coverage guarantees while improving conditional coverage. In addition, the approach naturally supports selective prediction, allowing unreliable claims or answer choices to be filtered out in downstream applications. We evaluate our approach on multiple white-box models across diverse domains and show that it significantly outperforms existing baselines in terms of conditional coverage.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

0 major / 3 minor

Summary. The manuscript proposes an adaptive conformal prediction approach for large language models that extends conformal score transformation methods to be prompt-dependent. This enables input-specific calibration for factuality uncertainty estimates in long-form generation and multiple-choice QA tasks. The method is claimed to retain the marginal coverage guarantees of standard conformal prediction while improving conditional coverage, and it naturally supports selective prediction by filtering unreliable outputs. Evaluations on multiple white-box LLMs across diverse domains show significant outperformance over existing non-adaptive baselines in conditional coverage metrics.

Significance. If the adaptive mechanism preserves marginal coverage while delivering the reported conditional coverage gains, the work would provide a practical advance in uncertainty quantification for LLMs, addressing a key limitation of prior conformal methods in handling prompt-dependent variability. The empirical results across models and tasks, combined with support for selective prediction, strengthen its potential utility in reliable generation pipelines. The paper's grounding in established conformal techniques is a positive aspect.

minor comments (3)
  1. The abstract and introduction would benefit from a brief statement of the precise form of the adaptive transformation (e.g., how prompt features enter the score function) to clarify the extension beyond prior work.
  2. In the experimental section, include explicit definitions or references for the conditional coverage metrics used, as well as the exact baseline implementations, to facilitate direct replication.
  3. Figure captions should specify the number of trials or seeds underlying the reported coverage curves to convey variability.

Simulated Author's Rebuttal

0 responses · 0 unresolved

We thank the referee for the thorough and positive review of our manuscript on adaptive conformal prediction for improving factuality in LLM generations. The recommendation for minor revision is appreciated, and we note that the summary accurately captures the core contributions regarding prompt-dependent calibration, retention of marginal guarantees, and support for selective prediction.

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper extends standard conformal prediction techniques to create a prompt-adaptive variant for LLM factuality assessment, explicitly retaining the marginal coverage guarantees of the base method while targeting improved conditional coverage. The abstract and described approach frame this as a direct methodological extension with empirical validation on white-box models and diverse tasks, without any self-definitional reductions, fitted parameters renamed as predictions, or load-bearing self-citations that collapse the central claim to prior inputs. The derivation remains self-contained against external conformal prediction benchmarks and does not invoke uniqueness theorems or ansatzes from the authors' own prior work in a circular manner.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only information prevents identification of concrete free parameters, axioms, or invented entities. The proposal appears to rest on standard conformal prediction assumptions about exchangeability and score functions, with adaptation likely introducing data-dependent fitting steps whose details are not visible.

pith-pipeline@v0.9.0 · 5471 in / 1073 out tokens · 39827 ms · 2026-05-10T13:13:04.945999+00:00 · methodology

discussion (0)

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Reference graph

Works this paper leans on

4 extracted references · 4 canonical work pages

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    Victoria Beckham

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    work page arXiv 2020