The CLRS-Text Algorithmic Reasoning Language Benchmark

Larisa Markeeva, Sean Michael McLeish, Borja Ibarz, Wilfried Bounsi, Olga Kozlova, Alex Vitvitskyi, Charles Blundell, Tom Goldstein, Avi Schwarzschild, Petar Veličković

Abstract: Eliciting reasoning capabilities from language models (LMs) is a critical direction on the path towards building intelligent systems. Many foundational 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. Several 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.

Keywords: large language models, algorithmic reasoning, out-of-distribution generalisation, length generalisation, multi-task learning, random positional embeddings

Code: https://github.com/google-deepmind/clrs/tree/master/clrs/_src/clrs_text

Assigned Action Editor: ~Christopher_De_Sa1

Submission Number: 114