Summary of The Clrs-text Algorithmic Reasoning Language Benchmark, by Larisa Markeeva et al.
The CLRS-Text Algorithmic Reasoning Language Benchmark
by Larisa Markeeva, Sean McLeish, Borja Ibarz, Wilfried Bounsi, Olga Kozlova, Alex Vitvitskyi, Charles Blundell, Tom Goldstein, Avi Schwarzschild, Petar Veličković
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Data Structures and Algorithms (cs.DS); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Most recent studies on reasoning capabilities in language models (LMs) focus on out-of-distribution performance on procedurally-generated synthetic benchmarks, making results difficult to transfer across publications. This trend slows down progress towards building intelligent systems. To address this issue, we propose CLRS-Text, a textual version of algorithmic traces from the Introduction to Algorithms textbook. CLRS-Text procedurally generates trace data for diverse, challenging algorithmic tasks across any input distribution, offering a standard pipeline for creating additional benchmark tasks. We fine-tune and evaluate various LMs as generalist executors on this benchmark, validating prior work and revealing a novel challenge for the LM reasoning community. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Eliciting reasoning capabilities from language models is important for building intelligent systems. Most studies focus on out-of-distribution performance on synthetic benchmarks. This makes it hard to compare results across different studies. We created CLRS-Text, a dataset that generates text based on algorithmic traces from a famous algorithms textbook. This helps us test how well LMs can reason and solve problems. We tested some LMs and found that they can do better than expected. |
