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LLMs in the Imaginarium: Tool Learning through Simulated Trial and Error
Tool-augmented large language models (LLMs) struggle with accurate tool use. A biologically inspired method, simulated trial and error (STE), improves tool learning, outperforming GPT-4 by 46.7%.
https://arxiv.org/abs//2403.04746
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
Tool-augmented large language models (LLMs) struggle with accurate tool use. A biologically inspired method, simulated trial and error (STE), improves tool learning, outperforming GPT-4 by 46.7%.
https://arxiv.org/abs//2403.04746
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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