# Doc2AHP: Inferring Structured Multi-Criteria Decision Models via Semantic Trees with LLMs

arXiv: https://arxiv.org/abs/2601.16479 · HTML: https://mlsanity.com/paper/2601.16479/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 36 references that are in the map.
- First posted on arXiv 23 Jan 2026.

## Builds on (most cited first, 16 of 16)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Tree of Thoughts: Deliberate Problem Solving with Large Language Models](https://mlsanity.com/paper/2305.10601/) (2023)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [From Local to Global: A Graph RAG Approach to Query-Focused Summarization](https://mlsanity.com/paper/2404.16130/) (2024)
- [From Amateurs to Connoisseurs: Modeling the Evolution of User Expertise through Online Reviews](https://mlsanity.com/paper/1303.4402/) (2013)
- [Debate or Vote: Which Yields Better Decisions in Multi-Agent Large Language Models?](https://mlsanity.com/paper/2508.17536/) (2025)
- [HotelRec: a Novel Very Large-Scale Hotel Recommendation Dataset](https://mlsanity.com/paper/2002.06854/) (2020)
- [Cascaded Language Models for Cost-effective Human-AI Decision-Making](https://mlsanity.com/paper/2506.11887/) (2025)
- [LLM Meeting Decision Trees on Tabular Data](https://mlsanity.com/paper/2505.17918/) (2025)
- [AHP-Powered LLM Reasoning for Multi-Criteria Evaluation of Open-Ended Responses](https://mlsanity.com/paper/2410.01246/) (2024)
- [Reliable Decision Making via Calibration Oriented Retrieval Augmented Generation](https://mlsanity.com/paper/2411.08891/) (2024)
- [Enhancing Multi-Criteria Decision Analysis with AI: Integrating Analytic Hierarchy Process and GPT-4 for Automated Decision Support](https://mlsanity.com/paper/2402.07404/) (2024)
- [One for All: A General Framework of LLMs-based Multi-Criteria Decision Making on Human Expert Level](https://mlsanity.com/paper/2502.15778/) (2025)
- [LLM-Assisted AHP for Explainable Cyber Range Evaluation](https://mlsanity.com/paper/2512.10487/) (2025)
- [Decision Making via AHP](https://mlsanity.com/paper/1402.1976/) (2014)
- [Transforming User Defined Criteria into Explainable Indicators with an Integrated LLM AHP System](https://mlsanity.com/paper/2601.05267/) (2025)

## Built on by (most cited first, 1 of 1)

- [Structured Multi-Criteria Evaluation of Large Language Models with Fuzzy Analytic Hierarchy Process and DualJudge](https://mlsanity.com/paper/2604.03742/) (2026)

## Abstract (arXiv)

While Large Language Models (LLMs) demonstrate remarkable proficiency in semantic understanding, they often struggle to ensure structural consistency and reasoning reliability in complex decision-making tasks that demand rigorous logic. Although classical decision theories, such as the Analytic Hierarchy Process (AHP), offer systematic rational frameworks, their construction relies heavily on labor-intensive domain expertise, creating an "expert bottleneck" that hinders scalability in general scenarios. To bridge the gap between the generalization capabilities of LLMs and the rigor of decision theory, we propose Doc2AHP, a novel structured inference framework guided by AHP principles. Eliminating the need for extensive annotated data or manual intervention, our approach leverages the structural principles of AHP as constraints to direct the LLM in a constrained search within the unstructured document space, thereby enforcing the logical entailment between parent and child nodes. Furthermore, we introduce a multi-agent weighting mechanism coupled with an adaptive consistency optimization strategy to ensure the numerical consistency of weight allocation. Empirical results demonstrate that Doc2AHP not only empowers non-expert users to construct high-quality decision models from scratch but also significantly outperforms direct generative baselines in both logical completeness and downstream task accuracy.