Authors: Natasha Butt, Blazej Manczak, Auke Wiggers, Corrado Rainone, David Zhang, Michaël Defferrard, Taco Cohen

Published on: February 07, 2024

Impact Score: 8.22

Arxiv code: Arxiv:2402.04858

Summary

• What is new: Introduction of the Code Iteration (CodeIt) method for language model self-improvement, marking a novel approach in the field.
• Why this is important: Large language models struggle with benchmarks of general intelligence such as the Abstraction and Reasoning Corpus (ARC).
• What the research proposes: The CodeIt method iterates between program sampling/hindsight relabeling and learning from prioritized experience replay to improve task-solving abilities.
• Results: CodeIt outperforms existing neural and symbolic baselines by solving 15% of ARC evaluation tasks, achieving state-of-the-art performance.

Technical Details

Technological frameworks used: CodeIt with prioritized experience replay and data-augmentation

Models used: Large language models

Data used: Abstraction and Reasoning Corpus (ARC)

Potential Impact

Educational technology, artificial intelligence research companies, and industries relying on complex problem-solving automation.

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