Long-range Language Modeling with Self-Retrieval
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Paper summary
Jointly trains a retrieval-augmented LM from scratch for long-range modeling.
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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.