From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution
Paper recorded by Signals 4 on 2026-09-02 in cs.AI. Abstract reproduced from arXiv; link to the original below.
Published 2026-09-02 on arXiv · recorded by Signals 4 on 2026-09-03
Category: cs.AI · 人工智能 · first seen 2026-09-03
Abstract
Training data attribution (TDA) aims to identify training examples that shape model behavior, but its intervention value depends on both which examples are selected and how they are modified. Influence functions (IF) estimate behavioral changes under infinitesimal reweighting, yet IF-selected examples often show limited advantages over random selection under conventional weight-based interventions
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Cite this page: From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution: the #209 most recent of 300 cs.AI papers we have recorded (as of 2026-09-02). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/from-reweighting-to-rewriting-unlocking-the-intervention-effects-of-influential-.html
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