MAREval: A Multi-Agent Framework for Evaluating Natural Language Recommendation Explanations

Authors: Reza Yousefi Maragheh , Jayesh Uddhav Kudase , Aysenur Inan , Ramin Giahi , Kai Zhao , Parth Parikh , Jianpeng Xu , Jason Cho , Evren Korpeoglu , Sushant Kumar

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Abstract: Evaluating natural language explanations in recommender systems is essential for fostering user trust, transparency, and engagement. However, existing evaluation approaches like human evaluations, while accurate, are resource-intensive and impractical at the scale required by modern recommendation platforms. Also, automated methods using single-agent LLMs suffer from prompt sensitivity and inconsistent outputs. To address these challenges, we propose MAREval, a structured multi-agent framework for evaluating recommendation explanations using large language models. MAREval orchestrates (i) a planner agent that uses a novel Chain of Debate (CoD) prompting strategy to coordinate agent roles and enforce logically consistent evaluation plans; (ii) a moderator agent that regulates discussions by mitigating prompt drift; and (iii) an arbitrator agent that aggregates outputs from multiple evaluation rounds and (iv) a Monte Carlo sampling method, improving robustness and alignment with human judgment. We conduct comprehensive evaluations on both public (TopicalChat) and proprietary recommendation datasets, demonstrating that MAREval outperforms state-of-the-art baselines. Comprehensive experiments on a public benchmark and a proprietary e-commerce dataset show that MAREval improves alignment with human judgments over strong singleand multi-agent baselines. Stability analyses indicate substantially lower variability across repeated trials. In a large human-annotation gate, MAREval meets production quality thresholds where prior evaluators fall short, and online A/B testing demonstrates statistically significant improvements in engagement and revenue metrics. These results establish MAREval as a scalable and reliable solution for human-aligned evaluation of recommendation explanations in real-world systems

Subjects:

Artificial Intelligence (cs.AI) ; Information Retrieval (cs.IR)

Cite as:

arXiv:2610.36389 [cs.AI]

(or

arXiv:2610.36389v1 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2610.36389

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arXiv-issued DOI via DataCite (pending registration)

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From: Reza Yousefi Maragheh [ view email ]

[v1]

Mon, 26 Oct 2026 23:14:07 UTC (6,766 KB)

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