LLM Attributor: Interactive Visual Attribution for LLM Generation
Seongmin Lee, Zijie J. Wang, Aishwarya Chakravarthy, Alec Helbling, ShengYun Peng, Mansi Phute, Duen Horng (Polo) Chau, Minsuk Kahng
Abstract
While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the importance of understanding the rationale behind text generation. We present LLM ATTRIBUTOR, a Python library that provides interactive visualizations for training data attribution of an LLM’s text generation. Our library offers a new way to quickly attribute an LLM’s text generation to training data points to inspect model behaviors, enhance its trustworthiness, and compare model-generated text with user-provided text. Thanks to LLM ATTRIBUTOR’s broad support for computational notebooks, users can easily integrate it into their workflow to interactively visualize attributions of their models.
BibTeX
@article{Lee_Wang_Chakravarthy_Helbling_Peng_Phute_Chau_Kahng_2025, title={LLM Attributor: Interactive Visual Attribution for LLM Generation}, volume={39}, url={https://ojs.aaai.org/index.php/AAAI/article/view/35357}, DOI={10.1609/aaai.v39i28.35357}, abstractNote={While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the importance of understanding the rationale behind text generation. We present LLM ATTRIBUTOR, a Python library that provides interactive visualizations for training data attribution of an LLM’s text generation. Our library offers a new way to quickly attribute an LLM’s text generation to training data points to inspect model behaviors, enhance its trustworthiness, and compare model-generated text with user-provided text. Thanks to LLM ATTRIBUTOR’s broad support for computational notebooks, users can easily integrate it into their workflow to interactively visualize attributions of their models.}, number={28}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Lee, Seongmin and Wang, Zijie J. and Chakravarthy, Aishwarya and Helbling, Alec and Peng, ShengYun and Phute, Mansi and Chau, Duen Horng (Polo) and Kahng, Minsuk}, year={2025}, month={Apr.}, pages={29655-29657} }