Retrieval from Within: An Intrinsic Capability of Attention-Based Models

Hugging Face Daily Papers Papers
retrieval-augmented-generation attention-models intrinsic-retrieval encoder-decoder question-answering evidence-recall

Summary

INTRA demonstrates that attention-based models can perform retrieval directly from internal representations, unifying retrieval and generation while improving evidence recall and answer quality.

Retrieval-augmented generation (RAG) typically treats retrieval and generation as separate systems. We ask whether an attention-based encoder-decoder can instead retrieve directly from its own internal representations. We introduce INTRA (INTrinsic Retrieval via Attention), a framework where decoder attention queries score pre-encoded evidence chunks that are then directly reused as context for generation. By construction, INTRA unifies retrieval and generation, eliminating the retriever-generator mismatch typical of RAG pipelines. This design also amortizes context encoding by reusing precomputed encoder states across queries. On question-answering benchmarks, INTRA outperforms strong engineered retrieval pipelines on both evidence recall and end-to-end answer quality. Our results demonstrate that attention-based models already possess a retrieval mechanism that can be elicited, rather than added as an external module.
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Source: https://huggingface.co/papers/2605.05806

Abstract

INTRA demonstrates that attention-based models can perform retrieval directly from internal representations, unifying retrieval and generation while improving evidence recall and answer quality.

Retrieval-augmented generation(RAG) typically treats retrieval and generation as separate systems. We ask whether anattention-based encoder-decodercan instead retrieve directly from its own internal representations. We introduce INTRA (INTrinsic Retrievalvia Attention), a framework wheredecoder attention queriesscorepre-encoded evidence chunksthat are then directly reused as context for generation. By construction, INTRA unifies retrieval and generation, eliminating theretriever-generator mismatchtypical of RAG pipelines. This design also amortizes context encoding by reusing precomputed encoder states across queries. On question-answering benchmarks, INTRA outperforms strong engineered retrieval pipelines on bothevidence recallandend-to-end answer quality. Our results demonstrate that attention-based models already possess a retrieval mechanism that can be elicited, rather than added as an external module.

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