LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages
<|reference_start|>LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in Low-Resource and Extinct Languages: In this paper, we present the LingOly benchmark, a novel benchmark for advanced reasoning abilities in large language models. Using challenging Linguistic Olympiad puzzles, we evaluate (i) capabilities for in-context identification and generalisation of linguistic patterns in very low-resource or extinct languages, and (ii) abilities to follow complex task instructions. The LingOly benchmark covers more than 90 mostly low-resource languages, minimising issues of data contamination, and contains 1,133 problems across 6 formats and 5 levels of human difficulty. We assess performance with both direct accuracy and comparison to a no-context baseline to penalise memorisation. Scores from 11 state-of-the-art LLMs demonstrate the benchmark to be challenging, and models perform poorly on the higher difficulty problems. On harder problems, even the top model only achieved 38.7% accuracy, 24.7% improvement over the no-context baseline. Large closed models typically outperform open models, and in general, the higher resource the language, the better the scores. These results indicate, in absence of memorisation, true multi-step out-of-domain reasoning remains a challenge for current language models.<|reference_end|>
@article{bean2024lingoly:,
    title={LINGOLY: A Benchmark of Olympiad-Level Linguistic Reasoning Puzzles in
  Low-Resource and Extinct Languages},
    author={Andrew M. Bean, Simi Hellsten, Harry Mayne, Jabez Magomere, Ethan A.
  Chi, Ryan Chi, Scott A. Hale, Hannah Rose Kirk},
    journal={arXiv preprint arXiv:2406.06196},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06196},
    primaryClass={cs.CL}
}