On the Merits of LLM-Based Corpus Enrichment

Gal Zur, Tommy Mordo, Moshe Tennenholtz, Oren Kurland

Abstract: Generative AI (genAI) technologies -- specifically, large language models (LLMs) -- and search have evolving relations. We argue for a novel perspective: using genAI to enrich a document corpus so as to improve query-based retrieval effectiveness. The enrichment is based on modifying existing documents or generating new ones. As an empirical proof of concept, we use LLMs to generate documents relevant to a topic which are more retrievable than existing ones. In addition, we demonstrate the potential merits of using corpus enrichment for retrieval augmented generation (RAG) and answer attribution in question answering.

External IDs:dblp:journals/corr/abs-2506-06015