No-Human in the Loop: Agentic Evaluation at Scale for Recommendation

• URL: http://arxiv.org/abs/2511.03051v1
• Date: Tue, 04 Nov 2025 22:49:39 GMT
• Title: No-Human in the Loop: Agentic Evaluation at Scale for Recommendation
• Authors: Tao Zhang, Kehui Yao, Luyi Ma, Jiao Chen, Reza Yousefi Maragheh, Kai Zhao, Jianpeng Xu, Evren Korpeoglu, Sushant Kumar, Kannan Achan,
• Abstract summary: evaluating large language models (LLMs) as judges is increasingly critical for building scalable and trustworthy evaluation pipelines.<n>We present ScalingEval, a large-scale benchmarking study that systematically compares 36 LLMs, including GPT, Gemini, Claude, and Llama.<n>Our multi-agent framework aggregates pattern audits and issue codes into ground-truth labels via scalable majority voting.
• Score: 11.764010898952677
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Evaluating large language models (LLMs) as judges is increasingly critical for building scalable and trustworthy evaluation pipelines. We present ScalingEval, a large-scale benchmarking study that systematically compares 36 LLMs, including GPT, Gemini, Claude, and Llama, across multiple product categories using a consensus-driven evaluation protocol. Our multi-agent framework aggregates pattern audits and issue codes into ground-truth labels via scalable majority voting, enabling reproducible comparison of LLM evaluators without human annotation. Applied to large-scale complementary-item recommendation, the benchmark reports four key findings: (i) Anthropic Claude 3.5 Sonnet achieves the highest decision confidence; (ii) Gemini 1.5 Pro offers the best overall performance across categories; (iii) GPT-4o provides the most favorable latency-accuracy-cost tradeoff; and (iv) GPT-OSS 20B leads among open-source models. Category-level analysis shows strong consensus in structured domains (Electronics, Sports) but persistent disagreement in lifestyle categories (Clothing, Food). These results establish ScalingEval as a reproducible benchmark and evaluation protocol for LLMs as judges, with actionable guidance on scaling, reliability, and model family tradeoffs.

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