Ce qui s'est passé: Elucidating the Design Space of LM Data Attribution

Researchers built a synthetic benchmark to evaluate how well language model data attribution methods identify which training examples influenced model outputs.

Pourquoi ça compte: Elucidating the Design Space of LM Data Attribution

Engineers auditing LM training data or building interpretability tools need reliable methods to trace predictions back to source documents.

Vigilance: Elucidating the Design Space of LM Data Attribution

The evaluation uses fictitious synthetic data, which may not fully capture attribution challenges in real-world training on authentic, naturally distributed documents.

Dans l'actualité

Elucidating the Design Space of LM Data Attribution

arXiv cs.AI · Publié le 29 octobre 2026 · 3 min de lecture

En 30 secondes

Ce qui s'est passé: Researchers built a synthetic benchmark to evaluate how well language model data attribution methods identify which training examples influenced model outputs.
Pourquoi ça compte: Engineers auditing LM training data or building interpretability tools need reliable methods to trace predictions back to source documents.
Vigilance: The evaluation uses fictitious synthetic data, which may not fully capture attribution challenges in real-world training on authentic, naturally distributed documents.

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llm
language model
retrieval
fine-tun
edge

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