RAG / 检索增强生成

ASVRI-Legal: Fine-Tuning LLMs with Retrieval Augmented Generation for Enhanced Legal Regulation

One Octadion, Bondan Sapta Prakoso, Nanang Yudi Setiawan, Novanto Yudistira

专题命中 检索器与排序 ：retrieval augmented generation(title)；retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL

Comments 11 pages (including references), 2 figures, 4 tables, published in Atlantis Press (Open Access under CC BY-NC 4.0 license)

Beyond Single Pass, Looping Through Time: KG-IRAG with Iterative Knowledge Retrieval

Ruiyi Yang, Hao Xue, Imran Razzak, Hakim Hacid, Flora D. Salim

机构 * University of New South Wales（新南威尔士大学） ； Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学） ； Technology Innovation Institute（技术创新研究所） 纠错

专题命中 检索器与排序 ：knowledge retrieval(title)；retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Comments 15 pages, 3 figures

Cache Mechanism for Agent RAG Systems

Shuhang Lin, Zhencan Peng, Lingyao Li, Xiao Lin, Xi Zhu, Yongfeng Zhang

机构 * Rutgers University（新泽西州立大学） ； University of South Florida（佛罗里达州立大学） ； University of Illinois Urbana–Champaign（伊利诺伊大学厄巴纳-香槟分校） 纠错

专题命中 检索器与排序 ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.CL

LGM: Enhancing Large Language Models with Conceptual Meta-Relations and Iterative Retrieval

Wenchang Lei, Ping Zou, Yue Wang, Feng Sun, Lei Zhao

机构 * Philisense（哲思科技） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments 30 pages, 5 figures

s3: You Don't Need That Much Data to Train a Search Agent via RL

Pengcheng Jiang, Xueqiang Xu, Jiacheng Lin, Jinfeng Xiao, Zifeng Wang, Jimeng Sun, Jiawei Han

机构 * University of Illinois Urbana Champaign（伊利诺伊大学厄巴纳-香槟分校） ； Amazon（亚马逊公司） 纠错

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.CL、cs.AI

Comments EMNLP 2025 camera-ready

Forecast2Anomaly (F2A): Adapting Multivariate Time Series Foundation Models for Anomaly Prediction

Atif Hassan, Tarun Kumar, Ashish Mishra, Sergey Serebryakov, Satish Kumar Mopur, Phanidhar Koganti, Murthy Chelankuri, Ramanagopal Vogety, Suparna Bhattacharya, Martin Foltin

专题命中 检索器与排序 ：retrieval-augmented generation(abstract)；RAG(abstract)；分类 cs.AI

Divide by Question, Conquer by Agent: SPLIT-RAG with Question-Driven Graph Partitioning

Ruiyi Yang, Hao Xue, Imran Razzak, Shirui Pan, Hakim Hacid, Flora D. Salim

机构 * University of New South Wales（新南威尔士大学） ； Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学） ； Griffith University（格里菲斯大学） ； Technology Innovation Institute（技术创新研究所） 纠错

专题命中 长文档RAG ：RAG(title,abstract)；retrieval-augmented generation(abstract)；分类 cs.IR、cs.AI

Comments 20 pages, 4 figures

Comparing the Performance of LLMs in RAG-based Question-Answering: A Case Study in Computer Science Literature

Ranul Dayarathne, Uvini Ranaweera, Upeksha Ganegoda

机构 * University of Moratuwa（穆塔瓦大学） 纠错

专题命中 RAG评测 ：RAG(title,abstract)；retrieval augmented generation(abstract)；分类 cs.CL、cs.AI

Comments 18 pages, 4 figures, 5 tables, presented at the 5th International Conference on Artificial Intelligence in Education Technology

Journal ref Lecture Notes on Data Engineering and Communications Technologies, vol. 228, Springer, 2025, pp. 387--403

Beyond Synthetic Benchmarks: Evaluating LLM Performance on Real-World Class-Level Code Generation

Musfiqur Rahman, SayedHassan Khatoonabadi, Emad Shihab

机构 * Concordia University（康科德大学） 纠错

专题命中 RAG评测 ：retrieval-augmented generation(abstract)；分类 cs.AI

Comments Pre-print submitted for reviwer to TOSEM