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Multi-LLM Adaptive Conformal Inference for Reliable LLM Response

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.0Download Report PDF
LLM Response FactualityConformal InferenceMulti-LLM
Abstract:

Ensuring factuality is essential for the safe use of Large Language Models (LLMs) in high-stakes domains such as medicine and law. Conformal inference provides distribution-free guarantees, but existing approaches are either overly conservative, discarding many true-claims, or rely on adaptive error rates and simple linear models that fail to capture complex group structures. To address these challenges, we reformulate conformal inference in a multiplicative filtering setting, modeling factuality as a product of claim-level scores. Our method, Multi-LLM Adaptive Conformal Inference MACI, leverages ensembles to produce more accurate factuality scores, which in our experiments led to higher retention, while validity is preserved through group-conditional calibration. Experiments show that MACI consistently achieves user-specified coverage with substantially higher retention and lower time cost than baselines. Our anonymized repository is available at https://github.com/Anonymous2026conf/MACI.git.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper introduces a multiplicative filtering framework for conformal inference in LLM factuality, leveraging multi-model ensembles to improve retention while preserving coverage guarantees. It resides in the 'Multi-Model and Ensemble-Based Conformal Inference' leaf, which contains only two papers total. This is a relatively sparse research direction within the broader taxonomy of 39 papers across 36 topics, suggesting that ensemble-based conformal methods for LLM factuality remain an emerging area. The sibling paper in this leaf explores aggregated calibration functions, indicating that the community has begun to recognize the value of combining multiple models but has not yet produced a large body of work in this specific niche.

The taxonomy tree reveals that neighboring leaves focus on single-model conformal prediction, claim decomposition with conformal guarantees, and abstention mechanisms. The paper's approach diverges from single-model methods by pooling evidence across multiple LLMs, and from abstention-focused frameworks by emphasizing retention rather than deferral. It shares conceptual ground with claim-level factuality methods, which also decompose outputs into atomic units, but differs by modeling factuality as a product of claim-level scores rather than applying conformal prediction to isolated claims. The scope note for this leaf explicitly excludes single-model methods and abstention-only frameworks, clarifying that the paper's ensemble-based strategy occupies a distinct methodological position.

Among the 15 candidates examined, none clearly refute the three core contributions. The multiplicative filtering framework was assessed against 2 candidates with no refutations, the theoretical retention analysis against 10 candidates with no refutations, and the MACI method with group-conditional calibration against 3 candidates with no refutations. This suggests that within the limited search scope, the paper's specific combination of multiplicative filtering, ensemble-based scoring, and group-conditional calibration does not have direct prior work. However, the small number of candidates examined (15 total) means the analysis covers a narrow slice of the literature, and a more exhaustive search could reveal additional overlapping methods or theoretical results.

Based on the top-15 semantic matches and the sparse taxonomy leaf, the work appears to occupy a relatively novel position in the ensemble-based conformal inference space. The limited search scope and the absence of refutable candidates suggest that the specific methodological contributions are not widely anticipated in the examined literature. However, the analysis does not cover the full breadth of conformal prediction or multi-model aggregation research, and the paper's novelty should be interpreted in light of this constraint.

Taxonomy

Core-task Taxonomy Papers
39
3
Claimed Contributions
15
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Ensuring factuality of large language model responses through conformal inference. The field has organized itself around several complementary directions. At the highest level, one finds frameworks that directly apply conformal prediction to control LLM factuality, including both single-model and ensemble-based approaches such as Multi-LLM Conformal[0] and AggLCF[39]. A second major branch focuses on retrieval-augmented generation with conformal guarantees, where methods like RAG Scoring Functions[31] and Principled Context Engineering[34] aim to certify the reliability of retrieved evidence. Uncertainty quantification and abstention mechanisms form another dense cluster, exemplified by works such as Conformal Abstention Policies[4] and Conformal Abstention Hallucinations[17], which decide when models should refrain from answering. Additional branches address multi-objective deployment (Conformal Alignment[10], Conformal Tail Risk[18]), task-specific applications (ConformalNL2LTL[11], Multiple-Choice Conformal[38]), domain-specific settings (EHR Conformal Prediction[12], Statistical Factuality VLM[25]), and theoretical innovations (Enhanced Conformal Validity[1], Conformal Language Modeling[2]). Within the ensemble-based conformal inference cluster, a handful of works explore how to aggregate predictions or calibration scores from multiple models to improve coverage and efficiency. Multi-LLM Conformal[0] sits squarely in this space, leveraging multiple LLMs to construct tighter prediction sets with statistical guarantees. This contrasts with single-model approaches like Conformal Factuality Guarantees[6] or Coherent Factuality Reasoning[7], which rely on internal model signals, and with abstention-focused methods such as Conformal Abstention Policies[4] that emphasize when to defer rather than how to combine evidence. A recurring theme across branches is the trade-off between coverage guarantees and practical efficiency: tighter sets often require more calibration data or computational overhead, while domain-shift robustness (Domain-Shift Conformal[5]) and conditional validity (Conditional Conformal Factuality[15]) remain open challenges. Multi-LLM Conformal[0] addresses efficiency by pooling information across models, positioning itself as a natural extension of foundational conformal methods into the multi-model regime.

Claimed Contributions

Multiplicative filtering framework for conformal inference

The authors reformulate conformal inference by modeling factuality as a cumulative product of claim-level scores rather than using a single global threshold. This framework preserves distribution-free, finite-sample coverage guarantees while enabling more flexible filtering.

2 retrieved papers
Theoretical retention analysis linking oracle-estimator deviations to true-claim preservation

The authors present a novel theoretical analysis showing how the gap between oracle and estimated factuality-scores affects the retention ratio of true claims. This analysis establishes a polynomial-rate bound under margin conditions and motivates the use of ensemble methods to reduce estimation error.

10 retrieved papers
Multi-LLM Adaptive Conformal Inference (MACI) method with group-conditional calibration

The authors develop MACI, which combines group-conditional conformal inference with a multi-LLM ensemble to achieve group-conditional coverage guarantees. The method uses ensemble-based factuality-scores and group-specific thresholds to maintain high retention while ensuring validity across different subgroups.

3 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Multiplicative filtering framework for conformal inference

The authors reformulate conformal inference by modeling factuality as a cumulative product of claim-level scores rather than using a single global threshold. This framework preserves distribution-free, finite-sample coverage guarantees while enabling more flexible filtering.

Contribution

Theoretical retention analysis linking oracle-estimator deviations to true-claim preservation

The authors present a novel theoretical analysis showing how the gap between oracle and estimated factuality-scores affects the retention ratio of true claims. This analysis establishes a polynomial-rate bound under margin conditions and motivates the use of ensemble methods to reduce estimation error.

Contribution

Multi-LLM Adaptive Conformal Inference (MACI) method with group-conditional calibration

The authors develop MACI, which combines group-conditional conformal inference with a multi-LLM ensemble to achieve group-conditional coverage guarantees. The method uses ensemble-based factuality-scores and group-specific thresholds to maintain high retention while ensuring validity across different subgroups.

Table of Contents

Multi-LLM Adaptive Conformal Inference for Reliable LLM Response | Novelty Validation