Aligning Large Language Models with Searcher Preferences
The paradigm shift from item-centric ranking to answer-centric synthesis is redefining the role of search engines. While recent industrial progress has applied generative techniques to closed-set item ranking in e-commerce, research and deployment of open-ended generative search on large content platforms remain limited. This setting introduces challenges, including robustness to noisy retrieval, non-negotiable safety guarantees, and alignment with diverse user needs. In this work, we introduce SearchLLM, the first large language model (LLM) for open-ended generative search. We design a hierarchical, multi-dimensional reward system that separates bottom-line constraints, including factual...
| Authors | Wei Wu, Peilun Zhou, Liyi Chen et al. |
|---|---|
| Primary category | cs.CL |
| Published | 11 March 2026 |
| Last updated at source | 11 March 2026 |
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