The CLRS-Text Algorithmic Reasoning Language Benchmark

• Main Authors: Markeeva, Larisa, McLeish, Sean, Ibarz, Borja, Bounsi, Wilfried, Kozlova, Olga, Vitvitskyi, Alex, Blundell, Charles, Goldstein, Tom, Schwarzschild, Avi, Veličković, Petar
• Format: Journal Article
• Language: English
• Published: 06.06.2024
• Subjects: Computer Science - Artificial Intelligence; Computer Science - Computation and Language; Computer Science - Data Structures and Algorithms; Computer Science - Learning; Statistics - Machine Learning
• DOI: 10.48550/arxiv.2406.04229

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.