Earticle

초록

영어

Large language models (LLMs) have demonstrated remarkable capabilities across a range of tasks, from complex reasoning to creative writing. Yet, these systems remain prone to hallucination, rely entirely on static training data, and risk homogenizing scientific thought through mode collapse. Retrieval-augmented generation (RAG) addresses these limitations by conditioning model outputs on dynamically retrieved external knowledge, tethering the immense generative capacity of LLMs to verifiable, human-curated corpora. This survey traces the development of RAG from its foundational formulations as a simple hallucination- mitigating pipeline to contemporary, highly adaptive architectures. We examine the core components—retrieval, re-ranking, and grounded generation—and survey advanced techniques including active retrieval, hierarchical memory, and multimodal extensions. Crucially, we highlight how LLM is evolving beyond a mere fact-checking mechanism into a collaborative, creative thinking partner. By feeding pluralistic evidence into the generation loop rather than relying on monolithic statistical patterns, multi-layered agentic RAG frameworks actively mitigate systemic biases, safeguard intellectual diversity, and amplify human reasoning. Ultimately, RAG represents an essential epistemological bridge for human-AI symbiosis, empowering users to make fairer and balanced decisions and accelerating the realization of co-prosperity in future society.

목차

Abstract
1. Introduction
2. Foundations of Retrieval-Augmented Generation
2.1. The Overview of RAG pipeline
2.2. Retrieval Paradigms and Re-ranking
2.3. Conditioned Gene
3. Advances in RAG
3.1. Retrieval Strategies
3.2. Architectural Innovations
3.3. Application Domains
3.4. Performance Trade-offs and Benchmarking
4. Multimodal and Cross-Domain Extensions
5. From Hallucination to Creativity
5.1. Hallucination mitigation
5.2. Interpretability and Evaluation
5.3. Bias and Homogeneity
6. Conclusions
References

키워드

저자

• Gawai Sneha Sanjay [ Department of Smart Information and Communication Engineering, Sun Moon University, Asan 31460, Korea ]
• Wonsang You [ Department of Data Science, Dongduk Women's University, Seoul 02748, Korea ]
• Jungwon Ryu [ Neuroscience-inspired Artificial Intelligence, KAIST, 291 Yuseong-gu, Daejeon 34141, Korea ] Corresponding Author

참고문헌

발행기관

간행물

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• 간행물명

  The Journal of Sciences and Innovation for Sustainable Peace(구 The journal of Hyojeong Academia)

• 간기

  반년간

• pISSN

  2982-9305

• 수록기간

  2023~2026

• 십진분류

  KDC 238 DDC 289