@inproceedings{yuan-etal-2026-llm,
    title = "{LLM}-Powered Benchmark Factory: Reliable, Generic, and Efficient",
    author = "Yuan, Peiwen  and
      Feng, Shaoxiong  and
      Li, Yiwei  and
      Wang, Xinglin  and
      Zhang, Yueqi  and
      Shi, Jiayi  and
      Tan, Chuyi  and
      Pan, Boyuan  and
      Hu, Yao  and
      Li, Kan",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1661/",
    pages = "35882--35903",
    ISBN = "979-8-89176-390-6",
    abstract = "The rapid advancement of large language models (LLMs) has led to a surge in both model supply and application demands. To facilitate effective matching between them, generic and efficient benchmark generators that can construct high-quality benchmarks are widely needed. However, human annotators are constrained by inefficiency, and current LLM-based benchmark generators lack not only generalizability but also a comprehensive evaluation framework for validation and optimization. To fill this gap, we first establish an automated evaluation framework, structured around four dimensions and ten criteria. Under this framework, we carefully analyze the advantages and weaknesses of directly prompting LLMs as generic benchmark generators. On this basis, we introduce a series of methods to address the identified weaknesses and integrate them as BenchMaker. Experiments across multiple LLMs and tasks confirm that BenchMaker achieves comparable performance to human-annotated benchmarks on most metrics, highlighting its generalizability and validity. More importantly, it delivers highly consistent evaluation results across 21 LLMs (e.g., 0.969 Pearson correlation against MMLU-Pro on language understanding task), while incurring minimal overhead (e.g., $0.005 and 0.38 minutes per sample when using GPT-4o mini as generator).$"
}