用检索增强生成技术让大模型回答更准确，避免胡说八道。
Deploying Large Language Models With Retrieval Augmented Generation
- 引入外部数据源，让大模型生成时有真实依据。
- 实测表明能显著提升回答的准确性与可信度。
- 适合需要合规与高可靠性的企业级信息应用。
大型语言模型（LLM）生成内容时常出现幻觉或非事实性回答，研究者日益关注如何将生成结果基于真实数据。检索增强生成（RAG）成为整合训练集外知识（如专有数据、实时信息）的关键方法。尽管诸多论文探讨不同RAG策略，其真实效能仍需在实际应用中验证。本文基于一个试点项目，展示将LLM与RAG结合用于信息检索的开发与现场测试经验，并分析该技术对信息价值链（人、流程、技术）的影响。旨在识别这一新兴技术在信息系统（IS）领域行为研究中的机遇与挑战。贡献包括制定最佳实践、采纳建议，以及提出一个确保符合行业监管要求的AI治理模型。
原文摘要 · Abstract (English)
Knowing that the generative capabilities of large language models (LLM) are sometimes hampered by tendencies to hallucinate or create non-factual responses, researchers have increasingly focused on methods to ground generated outputs in factual data. Retrieval Augmented Generation (RAG) has emerged as a key approach for integrating knowledge from data sources outside of the LLM's training set, including proprietary and up-to-date information. While many research papers explore various RAG strategies, their true efficacy is tested in real-world applications with actual data. The journey from conceiving an idea to actualizing it in the real world is a lengthy process. We present insights from the development and field-testing of a pilot project that integrates LLMs with RAG for information retrieval. Additionally, we examine the impacts on the information value chain, encompassing people, processes, and technology. Our aim is to identify the opportunities and challenges of implementing this emerging technology, particularly within the context of behavioral research in the information systems (IS) field. The contributions of this work include the development of best practices and recommendations for adopting this promising technology while ensuring compliance with industry regulations through a proposed AI governance model.
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