A proposed method prepends trainable [DUMP] tokens to input text to reduce representational interference in retrieval embedding models, addressing how fixed-size vector compression can entangle semantic content with residual token-level information such as artifacts from attention sink behavior. The approach targets the separation of semantic meaning from token-level noise in embeddings used for retrieval.
Embedding models compress variable-length text into fixed-size vector representations, but this compression can entangle semantic content with residual token-level information, such as artifacts arising from attention sink behavior. We propose a simple method that prepends trainable [DUMP] tokens to
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