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Long-range Language Modeling with Self-Retrieval

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Long-range Language Modeling with Self-Retrieval
Paper summary

Jointly trains a retrieval-augmented LM from scratch for long-range modeling.

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Key points
01

End-to-end retrieval training: Unlike retro-fitted RAG, trains the retriever and LM jointly from scratch for long-range consistency.

02

Long-form coherence: Targets tasks requiring retrieval of distant past context within a long document, not just factual lookup.

03

Architecture innovation: Introduces training procedures and architectural choices that make joint training stable and efficient.

04

Long-context RAG: Presaged the research direction of treating RAG and long-context as complementary rather than competing solutions.

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