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Large language models (LLMs) have demonstrated strong capabilities in question answering, yet they still frequently suffer from hallucinations on knowledge-intensive tasks. Knowledge graphs (KGs) provide LLMs with structured, interpretable, and updatable factual grounding, making them a promising external knowledge sou...
Crucially, the analysis reveals that the backbone encoder becomes self-contained post-training: the memory module facilitates the internalization of motif semantics into the model parameters, allowing the backbone to retain performance advantages even when the memory is detached during inference.
Crucially, the analysis reveals that the backbone encoder becomes self-contained post-training: the memory module facilitates the internalization of motif semantics into the model parameters, allowing the backbone to retain performance advantages even when the memory is detached during inference.
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