RecaLLM interleaves explicit in-context retrieval with reasoning to mitigate the lost-in-thought degradation in long-context LLMs, achieving gains on RULER and HELMET benchmarks with short training contexts.
Marc Forster
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RecaLLM: Addressing the Lost-in-Thought Phenomenon with Explicit In-Context Retrieval
RecaLLM interleaves explicit in-context retrieval with reasoning to mitigate the lost-in-thought degradation in long-context LLMs, achieving gains on RULER and HELMET benchmarks with short training contexts.