Papers with position-aware objective function

1 papers
Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models (2024.acl-long)

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Challenge: Existing methods for retrieval augmentation work with chunked contexts, which leads to poor quality of semantic representation and incomplete retrieval of useful information.
Approach: They propose a method for retrieval augmentation of long-context language modeling using landmark embedding.
Outcome: The proposed method outperforms existing retrieval methods with a notable advantage.

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