Attribute First, then Generate: Locally-attributable Grounded Text Generation
Topics: AI (Deep Learning), AIOverviews, LLMO, Retrieval Augmented Generation (RAG), SGE
The paper “Attribute First, then Generate: Locally-attributable Grounded Text Generation” proposes a novel method to address hallucinations in Large Language Models (LLMs) by ensuring concise and precise attributions in text generation. The method, tested on Multi-document Summarization (MDS) and Long-form Question-answering (LFQA), breaks down text generation into three steps: content selection, sentence planning, and sentence-by-sentence generation. This approach enhances both the quality of generated text and the accuracy of attributions while significantly reducing the time needed for human fact verification.
The authors of the paper “Attribute First, then Generate: Locally-attributable Grounded Text Generation” are:
- Aviv Slobodkin
- Eran Hirsch
- Arie Cattan
- Tal Schuster
- Ido Dagan
They are affiliated with Bar-Ilan University and Google Research.
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