对比微调与RAG，找到小模型对话中知识与风格的平衡点
KnowsLM: A framework for evaluation of small language models for knowledge augmentation and humanised conversations
- 通过调节LoRA秩、数据规模和提示前缀，测试小模型表现
- 微调提升风格一致性，RAG在新知识上更准确
- 适合需要真实知识更新或个性化语气的场景
在对话式AI不断发展的背景下，使用小型和中型语言模型生成简洁、上下文感知且类人对话仍是复杂挑战。本研究探讨了LoRA秩、数据集规模和提示前缀设计对知识保留与风格一致性的影响力。微调虽能提升流畅性并实现风格定制，但对未见知识的整合能力受限——尤其在小数据集下。相反，配备推理时引入外部文档能力的RAG增强模型，在分布外提示上表现出更优的事实准确性，但缺乏微调带来的风格一致性。基于大模型评委的评估显示，微调更适合语气适配，而RAG在实时知识增强方面表现更佳。
原文摘要 · Abstract (English)
In the evolving landscape of conversational AI, generating concise, context-aware, and human-like dialogue using small and medium-sized language models (LLMs) remains a complex challenge. This study investigates the influence of LoRA rank, dataset scale, and prompt prefix design on both knowledge retention and stylistic alignment. While fine-tuning improves fluency and enables stylistic customization, its ability to integrate unseen knowledge is constrained -- particularly with smaller datasets. Conversely, RAG-augmented models, equipped to incorporate external documents at inference, demonstrated superior factual accuracy on out-of-distribution prompts, though they lacked the stylistic consistency achieved by fine-tuning. Evaluations by LLM-based judges across knowledge accuracy, conversational quality, and conciseness suggest that fine-tuning is best suited for tone adaptation, whereas RAG excels at real-time knowledge augmentation.
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