  Verifiable generation requires large language models (LLMs) to cite source
documents supporting their outputs, thereby improve output transparency and
trustworthiness. Yet, previous work mainly targets the generation of
sentence-level citations, lacking specificity about which parts of a sentence
are backed by the cited sources. This work studies verifiable generation with
subsentence-level fine-grained citations for more precise location of generated
content supported by the cited sources. We first present a dataset, SCiFi,
comprising 10K Wikipedia paragraphs with subsentence-level citations. Each
paragraph is paired with a set of candidate source documents for citation and a
query that triggers the generation of the paragraph content. On SCiFi, we
evaluate the performance of state-of-the-art LLMs and strategies for processing
long documents designed for these models. Our experiment results reveals key
factors that could enhance the quality of citations, including the expansion of
the source documents' context accessible to the models and the implementation
of specialized model tuning.
