PoQ-Judge: New Framework for Cost-Efficient Quality Evaluation in Decentralized LLM Networks
Researchers have introduced PoQ-Judge, a multi-architecture framework that trains dedicated judge models to evaluate the quality of large language model outputs without requiring reference answers. The system was designed to address a core challenge in decentralized LLM inference networks, where lightweight, reference-free quality scoring is needed for Proof-of-Quality mechanisms. The work is significant because it demonstrates that reference-free evaluation can match the performance of reference-based approaches while substantially reducing computational cost.
PoQ-Judge is a proposed evaluation framework for decentralized LLM inference networks that trains judge models to score query-output pairs without access to ground-truth reference answers. The researchers tested three architectures spanning a quality-cost tradeoff: a TextCNN judge, a MiniLM cross-encoder, and a DeBERTa judge. Training used a two-stage process combining the UltraFeedback dataset with GPT-labeled in-domain data. The best-performing model achieved a Pearson correlation of 0.747 with a ground-truth proxy on a held-out test set, outperforming reference-based evaluators from prior work. As a reference-free component within a composite scoring system, it reached 0.645 Pearson correlation, matching the best single reference-based evaluator. A cascade evaluation strategy was shown to reduce inference cost by 72.7 percent with only modest quality loss, and online calibration identified semantic quality as the dominant scoring dimension. The authors note that performance is considerably stronger on question-answering tasks than on summarization, attributing this gap primarily to limitations in the quality of the ground-truth proxy rather than the judge models themselves.
What's missing
The paper does not report results on real-world decentralized network deployments, leaving open questions about practical performance under adversarial or distribution-shifted conditions. No independent replication or peer review beyond arXiv preprint status has been completed.
What different sources said
- arXiv cs.AICenter
PoQ-Judge: A Multi-Architecture Evaluation Framework for Cost-Aware Proof-of-Quality in Decentralized LLM Inference
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