  A recent focus of large language model (LLM) development, as exemplified by
generative search engines, is to incorporate external references to generate
and support its claims. However, evaluating the attribution, i.e., verifying
whether the generated statement is fully supported by the cited reference,
remains an open problem. Although human evaluation is common practice, it is
costly and time-consuming. In this paper, we investigate the automatic
evaluation of attribution given by LLMs. We begin by defining different types
of attribution errors, and then explore two approaches for automatic
evaluation: prompting LLMs and fine-tuning smaller LMs. The fine-tuning data is
repurposed from related tasks such as question answering, fact-checking,
natural language inference, and summarization. We manually curate a set of test
examples covering 12 domains from a generative search engine, New Bing. Our
results on this curated test set and simulated examples from existing
benchmarks highlight both promising signals and challenges. We hope our problem
formulation, testbeds, and findings will help lay the foundation for future
studies on this important problem.
