One Model Is Enough: Native Retrieval Embeddings from LLM Agent Hidden States | ScienceToStartup | ScienceToStartup

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References (9)

[1]

NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models

2024Chankyu Lee, Rajarshi Roy et al.

[2]

Precise Zero-Shot Dense Retrieval without Relevance Labels

2022Luyu Gao, Xueguang Ma et al.

[3]

Open-Domain Question Answering Goes Conversational via Question Rewriting

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"Equip LLM agents with native retrieval by projecting hidden states into the embedding space, eliminating the need for a separate embedding model and reducing latency."

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One Model Is Enough: Native Retrieval Embeddings from LLM Agent Hidden States

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References (9)

Founder's Pitch

"Equip LLM agents with native retrieval by projecting hidden states into the embedding space, eliminating the need for a separate embedding model and reducing latency."

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