# textttdattri: A Library for Efficient Data Attribution

arXiv: https://arxiv.org/abs/2410.04555 · HTML: https://mlsanity.com/paper/2410.04555/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 44 references that are in the map.
- First posted on arXiv 6 Oct 2024.

## Builds on (most cited first, 24 of 24)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Scaling Laws for Neural Language Models](https://mlsanity.com/paper/2001.08361/) (2020)
- [Understanding Black-box Predictions via Influence Functions](https://mlsanity.com/paper/1703.04730/) (2017)
- [Data Shapley: Equitable Valuation of Data for Machine Learning](https://mlsanity.com/paper/1904.02868/) (2019)
- [Estimating Training Data Influence by Tracing Gradient Descent](https://mlsanity.com/paper/2002.08484/) (2020)
- [Captum: A unified and generic model interpretability library for PyTorch](https://mlsanity.com/paper/2009.07896/) (2020)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [TRAK: Attributing Model Behavior at Scale](https://mlsanity.com/paper/2303.14186/) (2023)
- [Towards Efficient Data Valuation Based on the Shapley Value](https://mlsanity.com/paper/1902.10275/) (2019)
- [Influence Functions in Deep Learning Are Fragile](https://mlsanity.com/paper/2006.14651/) (2020)
- [Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset](https://mlsanity.com/paper/1810.12247/) (2018)
- [Music Transformer](https://mlsanity.com/paper/1809.04281/) (2018)
- [Datamodels: Predicting Predictions from Training Data](https://mlsanity.com/paper/2202.00622/) (2022)
- [Representer Point Selection for Explaining Deep Neural Networks](https://mlsanity.com/paper/1811.09720/) (2018)
- [Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms](https://mlsanity.com/paper/1908.08619/) (2019)
- [If Influence Functions are the Answer, Then What is the Question?](https://mlsanity.com/paper/2209.05364/) (2022)
- [Scaling Up Influence Functions](https://mlsanity.com/paper/2112.03052/) (2021)
- [Data Banzhaf: A Robust Data Valuation Framework for Machine Learning](https://mlsanity.com/paper/2205.15466/) (2022)
- [DsDm: Model-Aware Dataset Selection with Datamodels](https://mlsanity.com/paper/2401.12926/) (2024)
- [Input Similarity from the Neural Network Perspective](https://mlsanity.com/paper/2102.05262/) (2021)
- [OpenDataVal: a Unified Benchmark for Data Valuation](https://mlsanity.com/paper/2306.10577/) (2023)
- [Revisiting Methods for Finding Influential Examples](https://mlsanity.com/paper/2111.04683/) (2021)
- [Rethinking Data Shapley for Data Selection Tasks: Misleads and Merits](https://mlsanity.com/paper/2405.03875/) (2024)
- [Efficient Ensembles Improve Training Data Attribution](https://mlsanity.com/paper/2405.17293/) (2024)

## Built on by (most cited first, 18 of 18)

- [Better Training Data Attribution via Better Inverse Hessian-Vector Products](https://mlsanity.com/paper/2507.14740/) (2025)
- [GraSS: Scalable Data Attribution with Gradient Sparsification and Sparse Projection](https://mlsanity.com/paper/2505.18976/) (2025)
- [Do Large Language Models (Really) Need Statistical Foundations?](https://mlsanity.com/paper/2505.19145/) (2025)
- [Taming Hyperparameter Sensitivity in Data Attribution: Practical Selection Without Costly Retraining](https://mlsanity.com/paper/2505.24261/) (2025)
- [Z0-Inf: Zeroth Order Approximation for Data Influence](https://mlsanity.com/paper/2510.11832/) (2025)
- [Position: Don't Just "Fix it in Post": A Science of AI Must Study Training Dynamics](https://mlsanity.com/paper/2606.06533/) (2026)
- [Bergson: An Open Source Library for Data Attribution](https://mlsanity.com/paper/2606.11660/) (2026)
- [Measuring Fine-Grained Relatedness in Multitask Learning via Data Attribution](https://mlsanity.com/paper/2505.21438/) (2025)
- [A Unified Theory of Random Projection for Influence Functions](https://mlsanity.com/paper/2602.10449/) (2026)
- [Analyzing Fairness of Neural Network Prediction via Counterfactual Dataset Generation](https://mlsanity.com/paper/2602.10457/) (2026)
- [ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability](https://mlsanity.com/paper/2505.06258/) (2025)
- [Integrated Influence: Data Attribution with Baseline](https://mlsanity.com/paper/2508.05089/) (2025)
- [Exploring Training Data Attribution under Limited Access Constraints](https://mlsanity.com/paper/2509.12581/) (2025)
- [Scalable Data Attribution via Forward-Only Test-Time Inference](https://mlsanity.com/paper/2511.19803/) (2025)
- [Comparing Explanations is Not Enough, Explain the Change: New Standards are Needed to Explain Behavioral Shifts in Large Language Models](https://mlsanity.com/paper/2602.02304/) (2026)
- [EvoSelect: Data-Efficient LLM Evolution for Targeted Task Adaptation](https://mlsanity.com/paper/2604.26170/) (2026)
- [Traceprop: Training Data Attribution in a Single Pass](https://mlsanity.com/paper/2609.32380/) (2026)
- [dattri-LLM: A Unified and Efficient Library for Training Data Attribution at LLM Scale](https://mlsanity.com/paper/2609.38767/) (2026)

## Abstract (arXiv)

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 textttdattri, an open-source data attribution library that addresses the above needs. Specifically, textttdattri highlights three novel design features. Firstly, textttdattri 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, textttdattri 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, textttdattri 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 textttdattri library, we are able to perform a comprehensive and fair benchmark analysis across a wide range of data attribution methods. The source code of textttdattri is available at https://github.com/TRAIS-Lab/dattri.