The CLRS-Text Algorithmic Reasoning Language Benchmark
<|reference_start|>The CLRS-Text Algorithmic Reasoning Language Benchmark: Eliciting reasoning capabilities from language models (LMs) is a critical direction on the path towards building intelligent systems. Most recent studies dedicated to reasoning focus on out-of-distribution performance on procedurally-generated synthetic benchmarks, bespoke-built to evaluate specific skills only. This trend makes results hard to transfer across publications, slowing down progress. Three years ago, a similar issue was identified and rectified in the field of neural algorithmic reasoning, with the advent of the CLRS benchmark. CLRS is a dataset generator comprising graph execution traces of classical algorithms from the Introduction to Algorithms textbook. Inspired by this, we propose CLRS-Text -- a textual version of these algorithmic traces. Out of the box, CLRS-Text is capable of procedurally generating trace data for thirty diverse, challenging algorithmic tasks across any desirable input distribution, while offering a standard pipeline in which any additional algorithmic tasks may be created in the benchmark. We fine-tune and evaluate various LMs as generalist executors on this benchmark, validating prior work and revealing a novel, interesting challenge for the LM reasoning community. Our code is available at https://github.com/google-deepmind/clrs/tree/master/clrs/_src/clrs_text.<|reference_end|>
@article{markeeva2024the,
    title={The CLRS-Text Algorithmic Reasoning Language Benchmark},
    author={Larisa Markeeva, Sean McLeish, Borja Ibarz, Wilfried Bounsi, Olga
  Kozlova, Alex Vitvitskyi, Charles Blundell, Tom Goldstein, Avi Schwarzschild,
  Petar Veliv{c}kovi'c},
    journal={arXiv preprint arXiv:2406.04229},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04229},
    primaryClass={cs.LG cs.AI cs.CL cs.DS stat.ML}
}