信任与准确性之辨：RAG系统中的基座LLM与指令微调LLM对比
A Tale of Trust and Accuracy: Base vs. Instruct LLMs in RAG Systems
AI总结：
该研究对比RAG系统中基座与指令微调大语言模型的表现，发现实验设置下基座模型平均性能优20%，挑战了指令微调LLM更适配RAG的主流认知，推动对RAG基础问题的深入探讨。
摘要：
检索增强生成（Retrieval Augmented Generation，RAG）是人工智能领域的一项重要进展，它结合了检索阶段与生成阶段，其中生成阶段通常由大语言模型（large language models，LLMs）驱动。当前RAG的通用做法是使用“指令微调型”LLM，这类模型通过监督训练进行微调以提升遵循指令的能力，并采用前沿技术对齐人类偏好。与普遍认知相反，我们的研究表明，在我们的实验设置下，基座模型在RAG任务中的表现平均优于其指令微调版本20%。这一发现挑战了指令微调LLM在RAG应用中更具优势的主流假设。进一步研究揭示了更复杂的情况，对RAG的基础层面提出了质疑，表明需要就该主题展开更广泛的讨论；或者正如弗洛姆所言：“仅看统计数据很少足以理解数字的含义”。
英文摘要：
Retrieval Augmented Generation (RAG) represents a significant advancement in artificial intelligence combining a retrieval phase with a generative phase, with the latter typically being powered by large language models (LLMs). The current common practices in RAG involve using "instructed" LLMs, which are fine-tuned with supervised training to enhance their ability to follow instructions and are aligned with human preferences using state-of-the-art techniques. Contrary to popular belief, our study demonstrates that base models outperform their instructed counterparts in RAG tasks by 20% on average under our experimental settings. This finding challenges the prevailing assumptions about the superiority of instructed LLMs in RAG applications. Further investigations reveal a more nuanced situation, questioning fundamental aspects of RAG and suggesting the need for broader discussions on the topic; or, as Fromm would have it, "Seldom is a glance at the statistics enough to understand the meaning of the figures".
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
