提出f-INE框架，单次训练即可稳定估计数据影响。
f-INE: A Hypothesis Testing Framework for Estimating Influence under Training Randomness
- 基于假设检验，显式建模训练随机性带来的波动
- 单次训练完成影响估计，效率显著提升
- 可有效识别恶意指令数据，适合数据清洗与行为归因
影响估计方法旨在通过量化单个样本对最终模型的影响来解释和调试机器学习。然而，现有方法在训练随机性下表现不稳定：同一数据点在不同训练轮次中可能时而关键、时而无关。这种不稳定性使数据清洗或数据筛选难以信赖。为此，本文提出f-influence——一种基于假设检验的新型影响估计框架，明确考虑训练随机性，并具备理想性质以支持可靠的影响评估。我们进一步设计了高效算法f-INE，可在一次训练运行中完成影响估计。最后，我们将f-INE扩展至Llama-3.1-8B模型，评估指令微调数据的影响，成功检测出能引导模型偏见的中毒样本，验证其在数据清理与模型行为归因中的实用性。
原文摘要 · Abstract (English)
Influence estimation methods promise to explain and debug machine learning by estimating the impact of individual samples on the final model. Yet, existing methods collapse under training randomness: the same example may appear critical in one run and irrelevant in the next. Such instability undermines their use in data curation or cleanup since it is unclear if we indeed deleted/kept the correct datapoints. To overcome this, we introduce *f-influence* -- a new influence estimation framework grounded in hypothesis testing that explicitly accounts for training randomness, and establish desirable properties that make it suitable for reliable influence estimation. We also design a highly efficient algorithm **f**-**IN**fluence **E**stimation (**f-INE**) that computes f-influence **in a single training run**. Finally, we scale up f-INE to estimate influence of instruction tuning data on Llama-3.1-8B and show it can reliably detect poisoned samples that steer model opinions, demonstrating its utility for data cleanup and attributing model behavior.
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