Reducing Representational Interference in Retrieval Embeddings with Trainable Prefix Tokens
The paper proposes prepending trainable [DUMP] tokens to inputs during embedding model training to reduce residual token-level signals in retrieval representations.
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- Primary sourceHugging Face
- Discovery linkHugging Face
- Linked in sourceView arXiv page
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The paper proposes prepending trainable [DUMP] tokens to inputs during embedding model training to reduce residual token-level signals in retrieval representations. The method leaves architecture, pooling strategy, and training loss unchanged, optimizing the added tokens through the standard contrastive retrieval objective. The authors report consistent improvements over finetuned baselines on standard retrieval and long-context benchmarks across encoder and decoder embedding models, and analysis showing altered activation and attention patterns for low-information tokens.
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Trainable [DUMP] tokens are prepended to inputs during embedding model training, with architecture, pooling, and loss unchanged.
Source: Hugging Face ↗Stored source-based brief · key point 1 · AINewsIQ paraphraseThe authors report consistent gains over finetuned baselines on standard retrieval and long-context benchmarks.
Source: Hugging Face ↗Stored source-based brief · key point 2 · AINewsIQ paraphraseThe method was tested on both encoder and decoder embedding models.
Source: Hugging Face ↗Stored source-based brief · key point 3 · AINewsIQ paraphraseAnalysis indicates [DUMP] tokens alter activation and attention patterns associated with low-information tokens.
Source: Hugging Face ↗Stored source-based brief · key point 4 · AINewsIQ paraphraseLimits from the stored brief
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Why this may matter
If the gains hold, a lightweight training-time change could improve retrieval embeddings without modifying model architecture or inference pipelines, which matters for teams building search and RAG systems.
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