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SLM Meets LLM: Balancing Latency, Interpretability and Consistency in Hallucination Detection
Manage episode 436204834 series 3524393
This paper presents a framework using a small language model for initial hallucination detection, followed by a large language model for detailed explanations, optimizing real-time interpretable detection.
https://arxiv.org/abs//2408.12748
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
--- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support
1649 episodes
Manage episode 436204834 series 3524393
This paper presents a framework using a small language model for initial hallucination detection, followed by a large language model for detailed explanations, optimizing real-time interpretable detection.
https://arxiv.org/abs//2408.12748
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
--- Support this podcast: https://podcasters.spotify.com/pod/show/arxiv-papers/support
1649 episodes
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