AutoQABench: A Three-Level UX Benchmark for Automated Evaluation of Open-Ended LLM Responses
Evaluating open-ended large language model responses remains difficult because response quality depends not only on factual correctness and task completion, but also on subjective and scenario-dependent user experience factors. Existing benchmarks and automatic evaluators are effective for coarse-grained assessment, yet often provide limited insight into how different types of quality failures affect user-perceived response quality. To address this gap, we propose AutoQABench, an initial three-level user experience benchmark for automated evaluation of open-ended LLM responses. The benchmark decomposes response quality into three progressively organized levels: basic acceptability constraints, scenario-specific task effectiveness, and preference-sensitive experiential quality. Based on this design, we construct a dataset covering representative scenarios, including summarization, elaboration, emotional support, and responses to misleading premises, together with expert-defined evaluation standards. We also develop an LLM-based modular evaluator that performs stepwise assessment and generates both intermediate judgments and a final rating. Experimental results show that AutoQABench achieves good agreement with expert judgments and supports analysis at the module, scenario, and final-grade levels. Rather than replacing human evaluation, AutoQABench is intended as a scalable auxiliary evaluator that helps identify overlooked risks, task-completion failures, and quality differences in open-ended LLM interactions.