aiqbench analysis
This research proposes INTRA, a framework that enables attention-based models to retrieve information directly from their internal representations rather than relying on external retrieval-augmented generation (RAG) pipelines. By unifying retrieval and generation within the model architecture, this approach eliminates the typical mismatch between retrievers and generators while improving evidence recall and answer quality on question-answering benchmarks. The study suggests that sophisticated retrieval capabilities are an intrinsic property of attention mechanisms that can be elicited through specific architectural designs.
Retrieval from Within: An Intrinsic Capability of Attention-Based Models
arXiv:2605.05806v3 Announce Type: replace Abstract: 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.
Abstract: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.
Machine Learning (cs.LG)
arXiv:2605.05806 [cs.LG]
(or arXiv:2605.05806v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2605.05806 Focus to learn more arXiv-issued DOI via DataCite
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