Aug 2026· Proceedings of the 32nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining V.2· pp. 13308-13313· 0 citations· 133 references
TL;DR
This tutorial provides an in-depth treatment of modern RAG based on AI-facilitated systematic analysis of ~2000 recent papers (2020--2026) and traces the RAG pipeline from its modular foundations through graph-enhanced reasoning to the latest RL-driven agentic architectures, covering each stage.
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.
Hallucinations in knowledge-intensive tasks are frequent in Large Language Models (LLM). This is alleviated by Retrieval-Augmented Generation (RAG) and multi-agent systems, which include external knowledge and tools. Nevertheless, the consistency of retrieved evidence and the tracing of the pipeline in multifaceted int...
Large Language Models (LLMs) have demonstrated remarkable fluency and versatility across natural language tasks but remain fundamentally limited by their static knowledge and susceptibility to hallucinations, especially in domains requiring up to date or attribute grounded information. Retrieval Augmented Generation (R...
Meghana Sunil, V. Shravya, Shravan Venkatraman et al.· 0 citations
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
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
Retrieval Augmented Generation (RAG) augments large language models using external knowledge at the inference stage to increase factual accuracy and reduce hallucinations. Most existing RAG models employ dense vectors for information retrieval, which facilitates semantic matching at the expense of increased computation...
Alex Rohith I, Abhilash S. Nath, S. S· 2026 6th International Confe...· 0 citations
Retrieval-Augmented Generation (RAG) has recently emerged as a powerful paradigm for extending large language models (LLMs) with access to external knowledge sources, thereby mitigating issues of hallucination, limited corpus coverage, and outdated information. This paper presents an optimized local knowledge-base RAG...
K. S. Geethu, Anuradha Anumolu, H. B. Laxmi et al.· 2026 International Conferenc...· 0 citations
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