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[QA] GEM: Empowering LLM for both Embedding Generation and Language Understanding
The paper introduces GEM, a self-supervised method enabling decoder-only LLMs to generate high-quality text embeddings, enhancing performance on embedding benchmarks while preserving original text generation capabilities.
https://arxiv.org/abs//2506.04344
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 GEM, a self-supervised method enabling decoder-only LLMs to generate high-quality text embeddings, enhancing performance on embedding benchmarks while preserving original text generation capabilities.
https://arxiv.org/abs//2506.04344
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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