arxiv: 2605.12139 · v1 · submitted 2026-05-12 · 💻 cs.AI

Recognition: no theorem link

BoolXLLM: LLM-Assisted Explainability for Boolean Models

Du Cheng , Serdar Kadioglu , Xin Wang

Authors on Pith no claims yet

Pith reviewed 2026-05-13 05:51 UTC · model grok-4.3

classification 💻 cs.AI

keywords explainable AIBoolean ruleslarge language modelsfeature selectionrule interpretationhybrid modelsdiscretization

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The pith

Integrating large language models into Boolean rule learning creates accessible explanations while keeping strong predictive performance.

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

The paper proposes BoolXLLM, a framework that adds large language models to the Boolean rule learning process. LLMs assist in picking important features from the domain, recommending thresholds to convert numbers into logical categories, and rewriting the resulting rules as natural language stories. If successful, this would let non-technical users understand and trust the model's decisions more easily. The early results indicate that accuracy stays competitive while interpretability rises.

Core claim

BoolXLLM integrates large language models into the BoolXAI pipeline at three points: using them to select domain-relevant features, to recommend semantically meaningful discretization thresholds for numerical attributes, and to compress and interpret the learned Boolean rules into global and local natural language explanations. This produces models that remain faithful to the underlying logic while offering human-readable narratives.

What carries the argument

BoolXLLM, the hybrid framework that embeds LLMs into feature selection, discretization recommendation, and rule-to-language translation for Boolean classifiers.

Load-bearing premise

LLMs can be trusted to select semantically meaningful features and propose unbiased discretization thresholds without introducing errors.

What would settle it

An experiment comparing the performance and human-rated quality of explanations from BoolXLLM against standard BoolXAI on benchmark datasets where feature importance is known.

Figures

Figures reproduced from arXiv: 2605.12139 by Du Cheng, Serdar Kadioglu, Xin Wang.

**Figure 1.** Figure 1: BOOLXLMM architecture highlighting three stages where LLMs are incorporated to enhance explainability: (1) LLM Feature Selection, which identifies semantically meaningful and business-relevant features; (2) LLM Threshold Recommendation, which proposes context-aware discretization thresholds for numerical variables to improve semantic clarity; (3) LLM-Assisted Rule Compression and Interpretation, which pro… view at source ↗

read the original abstract

Interpretable machine learning aims to provide transparent models whose decision-making processes can be readily understood by humans. Recent advances in rule-based approaches, such as expressive Boolean formulas (BoolXAI), offer faithful and compact representations of model behavior. However, for non-technical stakeholders, main challenges remain in practice: (i) selecting semantically meaningful features and (ii) translating formal logical rules into accessible explanations. In this work, we propose BoolXLLM , as a hybrid framework that integrates Large Language Models (LLMs) into the end-to-end pipeline of Boolean rule learning. We augment BoolXAI , an expressive Boolean rule-based classifier, with LLMs at three critical stages: (1) feature selection, where LLMs guide the identification of domain-relevant variables; (2) threshold recommendation, where LLMs propose semantically meaningful discretization strategies for numerical features; and (3) rule compression and interpretation, where Boolean rules are translated into natural language explanations at both global and local levels. This integration bridges formal, faithful explanations with human-understandable narratives. This allows build an explainable AI system that is both theoretically grounded and accessible to non-experts. Early empirical results demonstrate that LLM-assisted pipelines improve interpretability while maintaining competitive predictive performance. Our work highlights the promise of combining symbolic reasoning with language-based models for human-centered explainability.

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

2 major / 0 minor

Summary. The manuscript proposes BoolXLLM, a hybrid framework that augments the BoolXAI expressive Boolean rule-based classifier with LLMs at three stages: (1) LLM-guided selection of domain-relevant features, (2) LLM-proposed semantically meaningful discretization thresholds for numerical variables, and (3) translation of Boolean rules into natural-language global and local explanations. The central claim is that this integration produces faithful yet accessible explanations for non-technical stakeholders while preserving competitive predictive performance, with support cited from early empirical results.

Significance. If the empirical claims are substantiated, the work could meaningfully advance human-centered XAI by bridging the faithfulness of symbolic Boolean models with the accessibility of LLM-generated narratives. The absence of any reported metrics, baselines, datasets, ablation studies, or validation procedures for the LLM stages, however, prevents assessment of whether the claimed gains in interpretability and maintained accuracy are realized.

major comments (2)

[Abstract] Abstract: the statement that 'early empirical results demonstrate that LLM-assisted pipelines improve interpretability while maintaining competitive predictive performance' supplies no metrics, baselines, datasets, error bars, or methodological details. This omission is load-bearing for the central claim, as the reader's report and skeptic note correctly identify that without such evidence the performance and interpretability assertions cannot be evaluated.
[Framework description] Framework description (stages 1 and 2): the pipeline relies on LLMs to select features and propose discretization thresholds without any described controls for error propagation, such as expert/ground-truth validation of LLM outputs, ablation removing the LLM components, or sensitivity analysis to hallucinations or domain bias. If even modest errors at these stages alter the induced Boolean rules, both the interpretability gain and the 'maintained competitive performance' assertion become unsupported, as noted in the stress-test concern.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive feedback, which highlights important areas for strengthening the manuscript. We address each major comment point by point below and have revised the paper accordingly to improve clarity and support for the claims.

read point-by-point responses

Referee: [Abstract] Abstract: the statement that 'early empirical results demonstrate that LLM-assisted pipelines improve interpretability while maintaining competitive predictive performance' supplies no metrics, baselines, datasets, error bars, or methodological details. This omission is load-bearing for the central claim, as the reader's report and skeptic note correctly identify that without such evidence the performance and interpretability assertions cannot be evaluated.

Authors: We agree that the abstract's phrasing is insufficiently supported and risks overstating the preliminary findings. In the revised manuscript, we will update the abstract to remove the broad claim and instead state that preliminary experiments on two benchmark datasets indicate competitive accuracy with improved human readability of explanations, with full metrics, baselines, and details provided in Section 4. This change ensures the central claim is properly grounded without misrepresenting the current evidence. revision: yes
Referee: [Framework description] Framework description (stages 1 and 2): the pipeline relies on LLMs to select features and propose discretization thresholds without any described controls for error propagation, such as expert/ground-truth validation of LLM outputs, ablation removing the LLM components, or sensitivity analysis to hallucinations or domain bias. If even modest errors at these stages alter the induced Boolean rules, both the interpretability gain and the 'maintained competitive performance' assertion become unsupported, as noted in the stress-test concern.

Authors: The referee is correct that the current framework description omits explicit safeguards against LLM errors in stages 1 and 2. We will add a new subsection titled 'Mitigating LLM-Induced Errors' that details: (i) repeated prompting with consensus voting to reduce hallucinations, (ii) optional expert validation step for selected features and thresholds, (iii) planned ablation experiments comparing LLM-assisted pipelines against non-LLM baselines on the same datasets, and (iv) sensitivity tests varying LLM temperature and prompt phrasing. These revisions will directly address error propagation and provide the missing validation procedures. revision: yes

Circularity Check

0 steps flagged

No circularity: BoolXLLM is a high-level framework proposal without derivations or self-referential reductions

full rationale

The paper describes an integration of LLMs into an existing Boolean rule learner (BoolXAI) at three pipeline stages: feature selection, discretization thresholds, and natural-language rule translation. No equations, fitted parameters, or first-principles derivations appear in the provided text. The central claim—that LLM assistance improves interpretability while preserving competitive accuracy—is presented as an empirical observation from early results rather than a mathematical prediction derived from internal definitions. BoolXAI is invoked as an external component without any self-citation chain that would make the integration claim tautological. No self-definitional loops, fitted-input-as-prediction patterns, or ansatz smuggling via prior work are present. The framework remains self-contained against external benchmarks because its value rests on the proposed pipeline architecture and reported performance, not on any reduction of outputs to inputs by construction.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The proposal rests on the established capabilities of BoolXAI and general-purpose LLMs without introducing new free parameters, mathematical axioms, or postulated entities in the abstract.

axioms (2)

domain assumption Expressive Boolean formulas provide faithful and compact representations of model behavior
Invoked as the foundation from BoolXAI in the abstract
ad hoc to paper LLMs can identify domain-relevant features and propose semantically meaningful discretization strategies
Central premise for stages 1 and 2; no validation procedure described

pith-pipeline@v0.9.0 · 5540 in / 1331 out tokens · 57919 ms · 2026-05-13T05:51:54.690842+00:00 · methodology

discussion (0)

Reference graph

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