Unifying Corroborative and Contributive Attributions in Large Language
  Models
Theodora Worledge Judy Hanwen Shen Nicole Meister Caleb Winston Carlos Guestrin
As businesses, products, and services spring up around large language models,
the trustworthiness of these models hinges on the verifiability of their
outputs. However, methods for explaining language model outputs largely fall
across two distinct fields of study which both use the term "attribution" to
refer to entirely separate techniques: citation generation and training data
attribution. In many modern applications, such as legal document generation and
medical question answering, both types of attributions are important. In this
work, we argue for and present a unified framework of large language model
attributions. We show how existing methods of different types of attribution
fall under the unified framework. We also use the framework to discuss
real-world use cases where one or both types of attributions are required. We
believe that this unified framework will guide the use case driven development
of systems that leverage both types of attribution, as well as the
standardization of their evaluation.