LLM Attributor: Interactive Visual Attribution for LLM Generation
Seongmin Lee Zijie J. Wang Aishwarya Chakravarthy Alec Helbling ShengYun Peng Mansi Phute Duen Horng Chau Minsuk Kahng
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. We
describe the visual and interactive design of our tool and highlight usage
scenarios for LLaMA2 models fine-tuned with two different datasets: online
articles about recent disasters and finance-related question-answer pairs.
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. For easier access and extensibility, we open-source LLM
Attributor at https://github.com/poloclub/ LLM-Attribution. The video demo is
available at https://youtu.be/mIG2MDQKQxM.