Attribute First, then Generate: Locally-attributable Grounded Text Generation

Aviv Slobodkin, Eran Hirsch, Arie Cattan, Tal Schuster, Ido Dagan

Abstract:

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.