Papers with embedding-based in-context

1 papers
Embedding-based In-Context Prompt Training for Enhancing LLMs as Text Encoders (2026.findings-acl)

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Challenge: Large language models (LLMs) have been widely explored for embedding generation.
Approach: They propose an embedding-based in-context prompt training strategy that leverages in-constext learning to generate high-quality embeddables while reducing computational burden.
Outcome: The proposed method surpasses models trained on publicly available retrieval data and achieves state-of-the-art embedding performance on the MTEB benchmark.

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