dorsal/arxiv
View SchemaTransforming User Defined Criteria into Explainable Indicators with an Integrated LLM AHP System
| Authors | Geonwoo Bang, Dongho Kim, Moohong Min |
|---|---|
| Categories | |
| ArXiv ID | 2601.05267vv1 |
| URL | https://arxiv.org/abs/2601.05267 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Evaluating complex texts across domains requires converting user defined criteria into quantitative, explainable indicators, which is a persistent challenge in search and recommendation systems. Single prompt LLM evaluations suffer from complexity and latency issues, while criterion specific decomposition approaches rely on naive averaging or opaque black-box aggregation methods. We present an interpretable aggregation framework combining LLM scoring with the Analytic Hierarchy Process. Our method generates criterion specific scores via LLM as judge, measures discriminative power using Jensen Shannon distance, and derives statistically grounded weights through AHP pairwise comparison matrices. Experiments on Amazon review quality assessment and depression related text scoring demonstrate that our approach achieves high explainability and operational efficiency while maintaining comparable predictive power, making it suitable for real time latency sensitive web services.
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"date_created": "2026-02-17T05:53:03.734000Z",
"date_modified": "2026-02-17T05:53:03.734000Z",
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"abstract": "Evaluating complex texts across domains requires converting user defined criteria into quantitative, explainable indicators, which is a persistent challenge in search and recommendation systems. Single prompt LLM evaluations suffer from complexity and latency issues, while criterion specific decomposition approaches rely on naive averaging or opaque black-box aggregation methods. We present an interpretable aggregation framework combining LLM scoring with the Analytic Hierarchy Process. Our method generates criterion specific scores via LLM as judge, measures discriminative power using Jensen Shannon distance, and derives statistically grounded weights through AHP pairwise comparison matrices. Experiments on Amazon review quality assessment and depression related text scoring demonstrate that our approach achieves high explainability and operational efficiency while maintaining comparable predictive power, making it suitable for real time latency sensitive web services.",
"arxiv_id": "2601.05267",
"authors": [
"Geonwoo Bang",
"Dongho Kim",
"Moohong Min"
],
"categories": [
"cs.IR",
"cs.CL",
"cs.LG"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Transforming User Defined Criteria into Explainable Indicators with an Integrated LLM AHP System",
"url": "https://arxiv.org/abs/2601.05267",
"version": "v1"
},
"schema_id": "dorsal/arxiv",
"source": {
"execution_id": "728a2efe-c9c9-4e06-918a-0d0ceaa4a73a",
"id": "arXiv Dataset",
"type": "Model",
"variant": "snapshot-2026-01-17",
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