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The paper introduces SYNTRA, a method to reduce hallucination in large language models (LLMs) on abstractive summarization tasks. By optimizing the LLM's system message via prefix-tuning on a synthetic task, hallucination is reduced on real-world downstream tasks.
https://arxiv.org/abs//2310.06827
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
The paper introduces SYNTRA, a method to reduce hallucination in large language models (LLMs) on abstractive summarization tasks. By optimizing the LLM's system message via prefix-tuning on a synthetic task, hallucination is reduced on real-world downstream tasks.
https://arxiv.org/abs//2310.06827
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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