configuration-driven framework 1feature engineering 1generative retrieval 1online inference 1prompt generation 1
From the 1 of 16 linked papers with an AI index.
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cs.IR2026
Prompt Generation Technical Report
Dan Ou, Gui Ling, Hao Wan +25
The paper introduces Prompt Generation (PG), a configuration‑driven framework that separates feature processing from model architecture for generative retrieval systems, enabling f…
cs.IR2026
Rethinking Retrieval-Augmentation as Synthesis: A Query-Aware Context Merging Approach
Jiarui Guo, Yuemeng Xu, Zongwei Lv +6
Retrieval-Augmented Generation (RAG) enables Large Language Models (LLMs) to extend their existing knowledge by dynamically incorporating external information. However, practical d…
cs.IR2025
RecIS: Sparse to Dense, A Unified Training Framework for Recommendation Models
Hua Zong, Qingtao Zeng, Zhengxiong Zhou +31
In this paper, we propose RecIS, a unified Sparse-Dense training framework designed to achieve two primary goals: 1. Unified Framework To create a Unified sparse-dense training fra…