From Reweighting to Rewriting: Unlocking the Intervention Effects of Influential Samples in Training Data Attribution

arXiv:2609.02771 · cs.CL, cs.AI, cs.LG · Submitted 2026-09-02 · Read on arXiv

cs.CL, cs.AI, cs.LG

Submitted: 2026-09-02

Updated: 2026-09-02

Code: https://github.com/pomonam/kronfluence

Terminology

• Abstention — 63× in this paper · explained in 1 episode
• Influence Functions — 18× in this paper · explained in 2 episodes
• Benchmark — 16× in this paper · explained in 2 episodes
• Accuracy — 7× in this paper · explained in 1 episode
• Alignment — 5× in this paper · explained in 1 episode
• Fine-Tuning — 5× in this paper · explained in 5 episodes
• Generative AI — 5× in this paper · explained in 2 episodes
• Adam — 4× in this paper · explained in 2 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Hallucination — 2× in this paper · explained in 3 episodes
• Instruction Tuning — 2× in this paper · explained in 2 episodes
• LLM judge — 2× in this paper · explained in 2 episodes
• Transferability — 2× in this paper · explained in 4 episodes
• Adaptation — 1× in this paper · explained in 1 episode
• Adaptive Weighting — 1× in this paper · explained in 1 episode
• Agentic Coding — 1× in this paper · explained in 1 episode
• Calibration — 1× in this paper · explained in 7 episodes
• Computational Tractability — 1× in this paper · explained in 2 episodes
• Consistency — 1× in this paper · explained in 3 episodes
• Cross-Model Transfer — 1× in this paper · explained in 2 episodes
• Epistemic Uncertainty — 1× in this paper · explained in 9 episodes
• Hallucination Mitigation — 1× in this paper · explained in 2 episodes
• In International Conference on Machine Learning — 1× in this paper
• Interpretability — 1× in this paper · explained in 15 episodes
• Large Language Models (LLMs) — 1× in this paper · explained in 6 episodes
• Linear probe — 1× in this paper · explained in 2 episodes
• Supervised Fine-Tuning (SFT) — 1× in this paper · explained in 8 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Verifier — 1× in this paper · explained in 2 episodes

Sources

• Think you have Solved Question Answering? Try ARC, the AI2 Reasoning Challenge
• Training Verifiers to Solve Math Word Problems
• Language Models (Mostly) Know What They Know
• Concept Influence: Leveraging Interpretability to Improve Performance and Efficiency in Training Data Attribution
• 2 OLMo 2 Furious
• Infusion: Shaping Model Behavior by Editing Training Data via Influence Functions
• Uncertainty-Based Abstention in LLMs Improves Safety and Reduces Hallucinations

Related papers

• Exploring Solution Divergence and Its Effect on Large Language Model Problem Solving
• Ishigaki-IDS-Bench: A Benchmark for Generating Information Delivery Specification from BIM Information Requirements
• Subliminal Steering: Stronger Encoding of Hidden Signals
• MedStruct-S: A Benchmark for Key Discovery, Key-Conditioned QA and Semi-Structured Extraction from OCR Clinical Reports
• The End of Transformers? On Challenging Attention and the Rise of Sub-Quadratic Architectures
• Untangling the Mechanisms of Misleading Context in Medical Question Answering