From Relevance to Authority: Authority-aware Generative Retrieval in Web Search Engines
Generative information retrieval (GenIR) formulates the retrieval process as a text-to-text generation task, leveraging the vast knowledge of large language models. However, existing works primarily optimize for relevance while often overlooking document trustworthiness. This is critical in high-stakes domains like healthcare and finance, where relying solely on semantic relevance risks retrieving unreliable information. To address this, we propose an Authority-aware Generative Retriever (AuthGR), the first framework that incorporates authority into GenIR. AuthGR consists of three key components: (i) Multimodal Authority Scoring, which employs a vision-language model to quantify authority...
| Authors | Sunkyung Lee, Jihye Back, Donghyeon Jeon et al. |
|---|---|
| Primary category | cs.IR |
| Published | 15 April 2026 |
| Last updated at source | 19 April 2026 |
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