  Recent efforts to address hallucinations in Large Language Models (LLMs) have
focused on attributed text generation, which supplements generated texts with
citations of supporting sources for post-generation fact-checking and
corrections. Yet, these citations often point to entire documents or
paragraphs, burdening users with extensive verification work. In this paper, we
introduce a locally-attributable text generation approach, prioritizing concise
attributions. Our method, named "Attribute First, then Generate", breaks down
the conventional end-to-end generation process into three intuitive steps:
content selection, sentence planning, and sequential sentence generation. By
initially identifying relevant source segments ("select first") and then
conditioning the generation process on them ("then generate"), we ensure these
segments also act as the output's fine-grained attributions ("select" becomes
"attribute"). Tested on Multi-document Summarization and Long-form
Question-answering, our method not only yields more concise citations than the
baselines but also maintains - and in some cases enhances - both generation
quality and attribution accuracy. Furthermore, it significantly reduces the
time required for fact verification by human assessors.
