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Data attribution methods quantify the influence of training data on model outputs and are becoming increasingly relevant for a wide range of LLM research and applications, including dataset curation, model interpretability, data valuation.
Improvements to bm25 and language models examined
Andrew Trotman, Antti Puurula, and Blake Burgess · 2014
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The lambada dataset: Word prediction requiring a broad discourse context
Denis Paperno, German David Kruszewski Martel, Angeliki Lazaridou, Ngoc Pham Quan, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda Torrent, Fernández Raquel, et al · 2016
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Understanding black-box predictions via influence functions
Pang Wei Koh and Percy Liang · 2017
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Crowdsourcing multiple choice science questions
Johannes Welbl, Nelson F Liu, and Matt Gardner · 2017
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Think you have solved question answering? try arc, the ai2 reasoning challenge
Peter Clark, Isaac Cowhey, Oren Etzioni, Tushar Khot, Ashish Sabharwal, Carissa Schoenick, and Oyvind Tafjord · 2018
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Can a suit of armor conduct electricity? a new dataset for open book question answering
Todor Mihaylov, Peter Clark, Tushar Khot, and Ashish Sabharwal · 2018
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Boolq: Exploring the surprising difficulty of natural yes/no questions
Christopher Clark, Kenton Lee, Ming-Wei Chang, Tom Kwiatkowski, Michael Collins, and Kristina Toutanova · 2019
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Stochastic beams and where to find them: The gumbel-top-k trick for sampling sequences without replacement
Wouter Kool, Herke Van Hoof, and Max Welling · 2019
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Hellaswag: Can a machine really finish your sentence?
Rowan Zellers, Ari Holtzman, Yonatan Bisk, Ali Farhadi, and Yejin Choi · 2019
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Piqa: Reasoning about physical commonsense in natural language
Yonatan Bisk, Rowan Zellers, Jianfeng Gao, Yejin Choi, et al · 2020
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Scaling laws for neural language models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Estimating training data influence by tracing gradient descent
Garima Pruthi, Frederick Liu, Satyen Kale, and Mukund Sundararajan · 2020
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Optimizing data usage via differentiable rewards
Xinyi Wang, Hieu Pham, Paul Michel, Antonios Anastasopoulos, Jaime Carbonell, and Graham Neubig · 2020
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Training verifiers to solve math word problems
Karl Cobbe, Vineet Kosaraju, Mohammad Bavarian, Mark Chen, Heewoo Jun, Lukasz Kaiser, Matthias Plappert, Jerry Tworek, Jacob Hilton, Reiichiro Nakano, et al · 2021
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Measuring massive multitask language understanding, 2021
Dan Hendrycks, Collin Burns, Steven Basart, Andy Zou, Mantas Mazeika, Dawn Song, and Jacob Steinhardt · 2021
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Lora: Low-rank adaptation of large language models, 2021
Edward J. Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen · 2021
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An empirical comparison of instance attribution methods for NLP
Pouya Pezeshkpour, Sarthak Jain, Byron Wallace, and Sameer Singh · 2021
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Towards tracing knowledge in language models back to the training data
Ekin Akyurek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, and Kelvin Guu · 2022
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Tracing knowledge in language models back to the training data, 2022
Ekin Akyürek, Tolga Bolukbasi, Frederick Liu, Binbin Xiong, Ian Tenney, Jacob Andreas, and Kelvin Guu · 2022
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Scaling instruction-finetuned language models
Hyung Won Chung, Le Hou, Shayne Longpre, Barret Zoph, Yi Tay, William Fedus, Eric Li, Xuezhi Wang, Mostafa Dehghani, Siddhartha Brahma, Albert Webson, Shixiang Shane Gu, Zhuyun Dai, Mirac Suzgun, Xinyun Chen, Aakanksha Chowdhery, Sharan Narang, Gaurav Mishra, Adams Yu, Vincent Zhao, Yanping Huang, Andrew Dai, Hongkun Yu, Slav Petrov, Ed H. Chi, Jeff Dean, Jacob Devlin, Adam Roberts, Denny Zhou, Quoc V. Le, and Jason Wei · 2022
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Datamodels: Predicting predictions from training data, 2022
Andrew Ilyas, Sung Min Park, Logan Engstrom, Guillaume Leclerc, and Aleksander Madry · 2022
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Locating and editing factual associations in gpt
Kevin Meng, David Bau, Alex Andonian, and Yonatan Belinkov · 2022
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Training language models to follow instructions with human feedback
Long Ouyang, Jeffrey Wu, Xu Jiang, Diogo Almeida, Carroll Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, et al · 2022
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Challenging big-bench tasks and whether chain-of-thought can solve them
Mirac Suzgun, Nathan Scales, Nathanael Schärli, Sebastian Gehrmann, Yi Tay, Hyung Won Chung, Aakanksha Chowdhery, Quoc V Le, Ed H Chi, Denny Zhou, et al · 2022
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Pythia: A suite for analyzing large language models across training and scaling
Stella Biderman, Hailey Schoelkopf, Quentin Gregory Anthony, Herbie Bradley, Kyle O’Brien, Eric Hallahan, Mohammad Aflah Khan, Shivanshu Purohit, USVSN Sai Prashanth, Edward Raff, et al · 2023
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Enhancing chat language models by scaling high-quality instructional conversations
Ning Ding, Yulin Chen, Bokai Xu, Yujia Qin, Shengding Hu, Zhiyuan Liu, Maosong Sun, and Bowen Zhou · 2023
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Alpacafarm: A simulation framework for methods that learn from human feedback
Yann Dubois, Chen Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy S Liang, and Tatsunori B Hashimoto · 2023
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Studying large language model generalization with influence functions, 2023
Roger Grosse, Juhan Bae, Cem Anil, Nelson Elhage, Alex Tamkin, Amirhossein Tajdini, Benoit Steiner, Dustin Li, Esin Durmus, Ethan Perez, Evan Hubinger, Kamilė Lukošiūtė, Karina Nguyen, Nicholas Joseph, Sam McCandlish, Jared Kaplan, and Samuel R. Bowman · 2023
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Llama guard: Llm-based input-output safeguard for human-ai conversations, 2023
Hakan Inan, Kartikeya Upasani, Jianfeng Chi, Rashi Rungta, Krithika Iyer, Yuning Mao, Michael Tontchev, Qing Hu, Brian Fuller, Davide Testuggine, and Madian Khabsa · 2023
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ToxicChat: Unveiling hidden challenges of toxicity detection in real-world user-AI conversation
Zi Lin, Zihan Wang, Yongqi Tong, Yangkun Wang, Yuxin Guo, Yujia Wang, and Jingbo Shang · 2023
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A holistic approach to undesired content detection in the real world
Todor Markov, Chong Zhang, Sandhini Agarwal, Florentine Eloundou Nekoul, Theodore Lee, Steven Adler, Angela Jiang, and Lilian Weng · 2023
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Trak: Attributing model behavior at scale
Sung Min Park, Kristian Georgiev, Andrew Ilyas, Guillaume Leclerc, and Aleksander Madry · 2023
Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson · 2024
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XSTest: A test suite for identifying exaggerated safety behaviours in large language models
Paul Röttger, Hannah Kirk, Bertie Vidgen, Giuseppe Attanasio, Federico Bianchi, and Dirk Hovy · 2024
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Data advisor: Dynamic data curation for safety alignment of large language models
Fei Wang, Ninareh Mehrabi, Palash Goyal, Rahul Gupta, Kai-Wei Chang, and Aram Galstyan · 2024
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Greats: Online selection of high-quality data for llm training in every iteration
Jiachen Tianhao Wang, Tong Wu, Dawn Song, Prateek Mittal, and Ruoxi Jia · 2024
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Qurating: Selecting high-quality data for training language models
Alexander Wettig, Aatmik Gupta, Saumya Malik, and Danqi Chen · 2024
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Fine-tuning aligned language models compromises safety, even when users do not intend to!
Xiangyu Qi, Yi Zeng, Tinghao Xie, Pin-Yu Chen, Ruoxi Jia, Prateek Mittal, and Peter Henderson · 2023
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Quanda: An interpretability toolkit for training data attribution evaluation and beyond, 2024
Dilyara Bareeva, Galip Ümit Yolcu, Anna Hedström, Niklas Schmolenski, Thomas Wiegand, Wojciech Samek, and Sebastian Lapuschkin · 2024
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Lessons from the trenches on reproducible evaluation of language models
Stella Biderman, Hailey Schoelkopf, Lintang Sutawika, Leo Gao, Jonathan Tow, Baber Abbasi, Alham Fikri Aji, Pawan Sasanka Ammanamanchi, Sidney Black, Jordan Clive, et al · 2024
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Scalable influence and fact tracing for large language model pretraining, 2024
Tyler A. Chang, Dheeraj Rajagopal, Tolga Bolukbasi, Lucas Dixon, and Ian Tenney · 2024
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Jailbreakbench: An open robustness benchmark for jailbreaking large language models, 2024
Patrick Chao, Edoardo Debenedetti, Alexander Robey, Maksym Andriushchenko, Francesco Croce, Vikash Sehwag, Edgar Dobriban, Nicolas Flammarion, George J. Pappas, Florian Tramer, Hamed Hassani, and Eric Wong · 2024
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What is your data worth to gpt? llm-scale data valuation with influence functions
Sang Keun Choe, Hwijeen Ahn, Juhan Bae, Kewen Zhao, Minsoo Kang, Youngseog Chung, Adithya Pratapa, Willie Neiswanger, Emma Strubell, Teruko Mitamura, et al · 2024
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What is your data worth to gpt? llm-scale data valuation with influence functions, 2024
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Less: Selecting influential data for targeted instruction tuning, 2024
Mengzhou Xia, Sadhika Malladi, Suchin Gururangan, Sanjeev Arora, and Danqi Chen · 2024
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Rethinking data selection at scale: Random selection is almost all you need, 2024
Tingyu Xia, Bowen Yu, Kai Dang, An Yang, Yuan Wu, Yuan Tian, Yi Chang, and Junyang Lin · 2024
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GradSafe: Detecting jailbreak prompts for LLMs via safety-critical gradient analysis
Yueqi Xie, Minghong Fang, Renjie Pi, and Neil Gong · 2024
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Gradsafe: Detecting jailbreak prompts for llms via safety-critical gradient analysis, 2024
Yueqi Xie, Minghong Fang, Renjie Pi, and Neil Gong · 2024
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On the vulnerability of safety alignment in open-access LLMs
Jingwei Yi, Rui Ye, Qisi Chen, Bin Zhu, Siheng Chen, Defu Lian, Guangzhong Sun, Xing Xie, and Fangzhao Wu · 2024
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Mates: Model-aware data selection for efficient pretraining with data influence models, 2024
Zichun Yu, Spandan Das, and Chenyan Xiong · 2024
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Shieldgemma: Generative ai content moderation based on gemma, 2024
Wenjun Zeng, Yuchi Liu, Ryan Mullins, Ludovic Peran, Joe Fernandez, Hamza Harkous, Karthik Narasimhan, Drew Proud, Piyush Kumar, Bhaktipriya Radharapu, Olivia Sturman, and Oscar Wahltinez · 2024
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Harnessing diversity for important data selection in pretraining large language models
Chi Zhang, Huaping Zhong, Kuan Zhang, Chengliang Chai, Rui Wang, Xinlin Zhuang, Tianyi Bai, Jiantao Qiu, Lei Cao, Ju Fan, et al · 2024
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A survey of data attribution: Methods, applications, and evaluation in the era of generative ai, 2025
Junwei Deng, Yuzheng Hu, Pingbang Hu, Ting-wei Li, Shixuan Liu, Jiachen T. Wang, Dan Ley, Qirun Dai, Benhao Huang, Jin Huang, Cathy Jiao, Hoang Anh Just, Yijun Pan, Jingyan Shen, Yiwen Tu, Weiyi Wang, Xinhe Wang, Shichang Zhang, Shiyuan Zhang, Ruoxi Jia, Himabindu Lakkaraju, Hao Peng, Weijing Tang, Chenyan Xiong, Jieyu Zhao, Hanghang Tong, Han Zhao, and Jiaqi W. Ma · 2025
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Large-scale data selection for instruction tuning, 2025
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On the feasibility of in-context probing for data attribution
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