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Supervised Knowledge Makes Large Language Models Better In-context Learners
The paper introduces a framework that improves the generalizability and factuality of Large Language Models (LLMs) through task-specific fine-tuning and discriminative models, resulting in enhanced performance on various language tasks.
https://arxiv.org/abs//2312.15918
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 a framework that improves the generalizability and factuality of Large Language Models (LLMs) through task-specific fine-tuning and discriminative models, resulting in enhanced performance on various language tasks.
https://arxiv.org/abs//2312.15918
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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