Integrating the Analytic Hierarchy Process with Large Language Models for Transparent Multi-Criteria Decision-Making

Effective evaluation of large language models (LLMs) remains a critical bottleneck, as conventional direct scoring often yields inconsistent and opaque judgments. In this work, we adapt the Analytic Hierarchy Process (AHP) to LLM-based evaluation and, more importantly, propose a confidence-aware Fuzzy AHP (FAHP) extension that models epistemic uncertainty via triangular fuzzy numbers modulated by LLM-generated confidence scores. Systematically validated on JudgeBench, our structured approach decomposes assessments into explicit criteria and incorporates uncertainty-aware aggregation, producing more calibrated judgments. Extensive experiments demonstrate that both crisp and fuzzy AHP consistently outperform direct scoring across model scales and dataset splits, with FAHP showing superior stability in uncertain comparison scenarios. Building on these insights, we propose DualJudge, a hybrid framework inspired by Dual-Process Theory that adaptively fuses holistic direct scores with structured AHP outputs via consistency-aware weighting. DualJudge achieves state-of-the-art performance, underscoring the complementary strengths of intuitive and deliberative evaluation paradigms. These results establish uncertainty-aware structured reasoning as a principled pathway toward more reliable LLM assessment. Code is available at https://github.com/hreyulog/AHP_llm_judge.

Large Language Models are increasingly deployed for decision-making, yet their adoption in high-stakes domains remains limited by miscalibrated probabilities, unfaithful explanations, and inability to incorporate expert knowledge precisely. We propose IDEA, a framework that extracts LLM decision knowledge into an interpretable parametric model over semantically meaningful factors. Through joint learning of verbal-to-numerical mappings and decision parameters via EM, correlated sampling that preserves factor dependencies, and direct parameter editing with mathematical guarantees, IDEA produces calibrated probabilities while enabling quantitative human-AI collaboration. Experiments across five datasets show IDEA with Qwen-3-32B (78.6%) outperforms DeepSeek R1 (68.1%) and GPT-5.2 (77.9%), achieving perfect factor exclusion and exact calibration -- precision unattainable through prompting alone. The implementation is publicly available at https://github.com/leonbig/IDEA.

Large language models (LLMs) are increasingly used as reasoning modules in many applications, yet they often struggle to produce human-aligned decisions. Human-aligned decision-making requires accounting for both explicitly stated goals and latent user preferences that shape how ambiguous situations should be resolved. Existing approaches either learn these preferences through continual per-interaction feedback, which is costly for both the user and the agent, or keep them implicit in dialogue transcripts or example sets tied to a single model. We present CLIPR (Conversational Learning for Inferring Preferences and Reasoning), a framework that represents user's preferences as an explicit, compact set of natural-language rules used at inference time for human-aligned decision-making. CLIPR elicits these rules from conversational input, monitors decision accuracy, and consults the user when performance degrades unexpectedly. CLIPR revises the learned rules, based on the conversation, only if it does not reduce accuracy on scenarios already processed. Evaluations on three preference-ambiguous datasets show that CLIPR matches or outperforms baselines while using up to 96% fewer LLM calls than continual-feedback preference learning. Our results also show that rules learned by one LLM transfer unchanged to others, and that our approach can recover from corrupted rules. In a user study with 30 participants, CLIPR outperformed baselines and was rated as requiring less effort than continual-feedback learning.