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[short] Language Models as Compilers: Simulating Pseudocode Execution Improves Algorithmic Reasoning in Language Models
The paper introduces THINK-AND-EXECUTE, a framework for improving large language models' algorithmic reasoning by decomposing the process into task-level logic discovery and instance-specific execution, outperforming baselines.
https://arxiv.org/abs//2404.02575
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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By Igor Melnyk
5
33 ratings
The paper introduces THINK-AND-EXECUTE, a framework for improving large language models' algorithmic reasoning by decomposing the process into task-level logic discovery and instance-specific execution, outperforming baselines.
https://arxiv.org/abs//2404.02575
YouTube: https://www.youtube.com/@ArxivPapers
TikTok: https://www.tiktok.com/@arxiv_papers
Apple Podcasts: https://podcasts.apple.com/us/podcast/arxiv-papers/id1692476016
Spotify: https://podcasters.spotify.com/pod/show/arxiv-papers
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