arXiv AI
Robust LLM Data Selection via Content-Invariant Gradient Attribution
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Form Over Content In Gradient-Based Data Attribution Methods
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arXiv Machine Learning
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Data Attribution in Large Language Models via Bidirectional Gradient Optimization
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Hugging Face Trending Papers
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Elucidating the Design Space of LM Data Attribution
The paper introduces the Controlled Attribution Benchmark (CATT), a synthetic evaluation framework that fine‑tunes language models on entirely fictitious knowledge to provide clear ground truth for data attribution. By systematically varying single dataset properties—such as document redundancy and lexical distractors—CATT reveals that these factors consistently degrade attribution performance. The study also explores two underexamined design choices: using negative log‑odds gradients instead of cross‑entropy gradients, and aggregating only the top token attribution scores, which together improve ground‑truth recall by 35–72 percentage points and enhance counterfactual retraining performance on FTRACE‑TREX by up to 4.9×.
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ABLE: Representing and Mapping LLMs via Attribution-Based Large-model Embedding
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arXiv AI
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Interpreting Black-Box Large Language Models with Sentence-Level Energy Landscapes
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