AI Research
Capacity Allocation at the Source: Sparse Target Optimization for LLM Knowledge Editing
A research focus on efficient updates to knowledge embedded in large language models
Knowledge editing has emerged as a promising approach for efficiently updating embedded knowledge in large language models (LLMs).
AC
ArXiv CS.CL
Source
ArXiv CS.CL
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Generated insight by OnAirToday AI
Knowledge editing could offer a more efficient way to update information embedded in large language models. The title points to capacity allocation and sparse target optimization as the focus, but the supplied text gives no methods or results.
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