Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 13308-13313· 0 citations· 27 references
Abstract
Retrieval-Augmented Generation (RAG) has emerged as the dominant strategy to ground LLM outputs in retrieved evidence to reduce hallucinations and increase factuality, yet RAG itself introduces new challenges: noisy retrieval, knowledge conflicts, and wasted computation from unnecessary retrieval. This tutorial provides an in-depth treatment of modern RAG based on AI-facilitated systematic analysis of ~2000 recent papers (2020--2026). We trace the RAG pipeline from its modular foundations through graph-enhanced reasoning to the latest RL-driven agentic architectures, covering each stage---triggering, query rewriting, retrieval, post-processing, generation, and knowledge-graph integration---with running examples, method comparisons, and benchmark results. Attendees will leave with a structured roadmap, evidence-backed practical recommendations, and a clear map of open research opportunities.
The research findings show that RL has gradually expanded from simply improving the accuracy of the final answer to optimizing queries, multi-round search, process decision-making and trustworthy screening, providing new ideas for enhancing the active retrieval ability of RAG and improving the credibility of informatio...
Zun-Long Hong· Applied and Computational En...· 0 citations
Retrieval-Augmented Generation (RAG) has become a fundamental paradigm for enhancing Large Language Models (LLMs) with external knowledge. However, while recent structure-augmented approaches organize documents into graphs to improve information access, their retrieval strategies remain largely static, relying on simil...
Jinghong Lei, Wang Kun, Zhigang Chen et al.· Proceedings of the 32nd ACM...· 0 citations
An in-depth analysis of the evolution of Naïve RAG to Modular and Advanced RAG models, and the introduction of new innovations, such as self-reflection, dense vector recovery, and the use of different models.
Mallikarjunarao Sunke, S. Gudi, Sriharsha Gudi· International Journal for Re...· 0 citations
: The legal domain imposes unique demands on Large Language Models (LLMs), requiring high factual accuracy, precise statutory citation, and transparent reasoning. Retrieval-Augmented Generation (RAG) has emerged as a promising paradigm to mitigate hallucinations and improve knowledge grounding in legal applications. Wh...
Xin Li· Proceedings of the 3rd Inter...· 0 citations
Experimental results show that SAC-RAG reduces token consumption by 38%–58% at the cost of only a 1–2 percentage point EM drop, with EM actually improving after compression for reasoning-type questions, achieving the optimal quality–efficiency trade-off in terms of token consumption.
Deyu Zhang, Hongqiang Yu, Jinze Huo et al.· IEEE Access· 0 citations
A penalty-aware evaluation framework for deployed RAG products is presented, combining asymmetric scoring, knowledge-gap canaries, questions whose answers are verifiably absent from the knowledge base, so that any answer constitutes ungrounded generation from parametric memory, and a failure-attribution pipeline that s...
Alden Do Rosario, Hussein Younes, F. Pires· 1 citation
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