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This study examines Large Language Models' generalization strategies in reasoning tasks, revealing distinct data influences for factual versus reasoning questions, highlighting procedural knowledge's role in their reasoning processes.
https://arxiv.org/abs//2411.12580
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
By Igor Melnyk5
33 ratings
This study examines Large Language Models' generalization strategies in reasoning tasks, revealing distinct data influences for factual versus reasoning questions, highlighting procedural knowledge's role in their reasoning processes.
https://arxiv.org/abs//2411.12580
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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