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arXiv Machine Learning By Elad Hoffer, Yochai Blau, Edan Kinderman, Ron Banner, Daniel Soudry, Boris Ginsburg

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

Read the original on arXiv Machine Learning →

The paper introduces INTRA, an attention-based encoder-decoder framework that retrieves directly from its own internal representations instead of using an external retriever. By having decoder attention query pre-encoded evidence chunks, INTRA unifies retrieval and generation, eliminating the typical mismatch seen in retrieval-augmented generation pipelines. Experiments on question-answering benchmarks show that INTRA outperforms strong engineered retrieval pipelines in both evidence recall and overall answer quality.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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