@inproceedings{efrat-etal-2023-lmentry,
    title = "{LM}entry: A Language Model Benchmark of Elementary Language Tasks",
    author = "Efrat, Avia  and
      Honovich, Or  and
      Levy, Omer",
    editor = "Rogers, Anna  and
      Boyd-Graber, Jordan  and
      Okazaki, Naoaki",
    booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
    month = jul,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/moar-dois/2023.findings-acl.666/",
    doi = "10.18653/v1/2023.findings-acl.666",
    pages = "10476--10501",
    abstract = "As the performance of large language models rapidly improves, benchmarks are getting larger and more complex as well. We present LMentry, a benchmark that avoids this ``arms race'' by focusing on a compact set of tasks that are trivial to humans, e.g. writing a sentence containing a specific word, identifying which words in a list belong to a specific category, or choosing which of two words is longer.LMentry is specifically designed to provide quick and interpretable insights into the capabilities and robustness of large language models. Our experiments reveal a wide variety of failure cases that, while immediately obvious to humans, pose a considerable challenge for large language models, including OpenAI{'}s latest 175B-parameter instruction-tuned model, TextDavinci002.LMentry complements contemporary evaluation approaches of large language models, providing a quick, automatic, and easy-to-run ``unit test'', without resorting to large benchmark suites of complex tasks."
}