Controlled benchmark clarifies failures in language-model data attribution
The paper studies why language-model data-attribution methods fail, noting evaluations often vary several dataset properties at once. It introduces CATT, a controlled fact-tracing benchmark using fine-tuning on entirely fictitious knowledge with unambiguous ground truth. Why it matters Reliable attribution is needed to trace model outputs to training data, audit sources, and diagnose failures.…
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