Fuzzy-Cognitive Integration and LLM-Assisted Interpretation: Toward Traceable and Cognitively Faithful AI Explanations
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| Formato: | Recurso digital |
| Lenguaje: | inglés |
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2025
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| _version_ | 1866901703256702976 |
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| author | Oettgen, Walther Saulnier, Benjamin |
| author_facet | Oettgen, Walther Saulnier, Benjamin |
| contents | <p>This article presents a <strong>hybrid Fuzzy–LLM framework</strong> that bridges numerical modeling and linguistic reasoning to produce traceable, cognitively faithful explanations in sensory and cognitive modeling. Building upon the previous works (<em>Explainable Hybrid Modeling</em>, <em>Robust Explainable Modeling</em>, and <em>From Explainability to Prescription</em>), it introduces a new dataset, <strong>FuzzyCog-Sensory v1</strong>, combining physico-chemical variables, hedonic responses, and linguistic annotations.<br>The study evaluates how fuzzy rules derived from sensory data can be interpreted by <strong>Large Language Models (LLMs)</strong> under controlled traceability constraints. Quantitative metrics—<strong>Cognitive Fidelity Index (CFI)</strong>, <strong>Align-Score</strong>, and <strong>Bias Rate</strong>—measure the alignment between numerical dependencies and generated linguistic explanations. Results demonstrate that <strong>Fuzzy–LLM systems</strong> can generate consistent, auditable, and human-centered scientific narratives, advancing explainable AI toward true <strong>cognitive interpretability</strong>.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_17386634 |
| institution | Zenodo |
| language | eng |
| publishDate | 2025 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Fuzzy-Cognitive Integration and LLM-Assisted Interpretation: Toward Traceable and Cognitively Faithful AI Explanations Oettgen, Walther Saulnier, Benjamin Fuzzy Logic Cognitive Modeling Explainable AI Large Language Models Human–AI Co-Interpretation Cognitive Fidelity <p>This article presents a <strong>hybrid Fuzzy–LLM framework</strong> that bridges numerical modeling and linguistic reasoning to produce traceable, cognitively faithful explanations in sensory and cognitive modeling. Building upon the previous works (<em>Explainable Hybrid Modeling</em>, <em>Robust Explainable Modeling</em>, and <em>From Explainability to Prescription</em>), it introduces a new dataset, <strong>FuzzyCog-Sensory v1</strong>, combining physico-chemical variables, hedonic responses, and linguistic annotations.<br>The study evaluates how fuzzy rules derived from sensory data can be interpreted by <strong>Large Language Models (LLMs)</strong> under controlled traceability constraints. Quantitative metrics—<strong>Cognitive Fidelity Index (CFI)</strong>, <strong>Align-Score</strong>, and <strong>Bias Rate</strong>—measure the alignment between numerical dependencies and generated linguistic explanations. Results demonstrate that <strong>Fuzzy–LLM systems</strong> can generate consistent, auditable, and human-centered scientific narratives, advancing explainable AI toward true <strong>cognitive interpretability</strong>.</p> |
| title | Fuzzy-Cognitive Integration and LLM-Assisted Interpretation: Toward Traceable and Cognitively Faithful AI Explanations |
| topic | Fuzzy Logic Cognitive Modeling Explainable AI Large Language Models Human–AI Co-Interpretation Cognitive Fidelity |
| url | https://doi.org/10.5281/zenodo.17386634 |