$\texttt{dattri}$：一个用于高效数据归因的库
$\texttt{dattri}$: A Library for Efficient Data Attribution
AI总结：
针对当前缺乏综合数据归因开发与基准测试库的问题，研究团队推出开源库$\texttt{dattri}$，其具备统一易用API、模块化底层函数、含生成式AI场景的综合基准框架三大特性，已集成多种前沿高效归因方法，可支撑大规模神经网络模型的归因研究与公平基准对比。
摘要：
数据归因方法旨在量化单个训练样本对人工智能（AI）模型预测的影响。随着训练数据在大规模AI模型的现代开发中发挥着日益关键的作用，数据归因已在提升AI性能与安全性方面得到广泛应用。然而，尽管近期涌现出大量新的数据归因方法，目前仍缺乏一个可助力不同数据归因方法的开发、基准测试与部署的综合库。在本研究中，我们推出$\texttt{dattri}$——一款可满足上述需求的开源数据归因库。具体而言，$\texttt{dattri}$具备三大新颖设计特性。其一，$\texttt{dattri}$提出了统一且易用的API，用户仅需修改几行代码，即可将不同的数据归因方法集成到其基于PyTorch的机器学习流水线中。其二，$\texttt{dattri}$对数据归因方法中常用的底层效用函数（如海森向量积、逆海森向量积或随机投影）进行了模块化处理，让研究人员更易于开发新的数据归因方法。其三，$\texttt{dattri}$提供了一套综合基准框架，包含预训练模型以及适用于多种基准设置（含生成式AI设置）的真值标注。我们已在库中实现了多种可应用于大规模神经网络模型的前沿高效数据归因方法，并将在未来持续更新该库。借助所开发的$\texttt{dattri}$库，我们能够对各类数据归因方法开展全面且公平的基准分析。$\texttt{dattri}$的源代码可在https://github.com/TRAIS-Lab/dattri获取。
英文摘要：
Data attribution methods aim to quantify the influence of individual training samples on the prediction of artificial intelligence (AI) models. As training data plays an increasingly crucial role in the modern development of large-scale AI models, data attribution has found broad applications in improving AI performance and safety. However, despite a surge of new data attribution methods being developed recently, there lacks a comprehensive library that facilitates the development, benchmarking, and deployment of different data attribution methods. In this work, we introduce $\texttt{dattri}$, an open-source data attribution library that addresses the above needs. Specifically, $\texttt{dattri}$ highlights three novel design features. Firstly, $\texttt{dattri}$ proposes a unified and easy-to-use API, allowing users to integrate different data attribution methods into their PyTorch-based machine learning pipeline with a few lines of code changed. Secondly, $\texttt{dattri}$ modularizes low-level utility functions that are commonly used in data attribution methods, such as Hessian-vector product, inverse-Hessian-vector product or random projection, making it easier for researchers to develop new data attribution methods. Thirdly, $\texttt{dattri}$ provides a comprehensive benchmark framework with pre-trained models and ground truth annotations for a variety of benchmark settings, including generative AI settings. We have implemented a variety of state-of-the-art efficient data attribution methods that can be applied to large-scale neural network models, and will continuously update the library in the future. Using the developed $\texttt{dattri}$ library, we are able to perform a comprehensive and fair benchmark analysis across a wide range of data attribution methods. The source code of $\texttt{dattri}$ is available at https://github.com/TRAIS-Lab/dattri.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
