用小模型+检索增强，让汽车硬件测试更高效
Smarter, not Bigger: Fine-Tuned RAG-Enhanced LLMs for Automotive HIL Testing
- 用领域数据微调小模型，结合语义检索提升测试用例召回
- 微调后的紧凑模型在准确率、延迟和成本上优于大模型
- 用户测试显示增强助手更可信、更实用，适合工业场景
硬件在环（HIL）测试对汽车验证至关重要，但测试文档分散且利用率低。本文提出HIL-GPT，一种融合领域适配大语言模型与语义检索的检索增强生成系统。该系统通过启发式挖掘和大模型辅助合成构建领域专用数据集，对嵌入模型进行微调，并采用向量索引实现可扩展、可追溯的测试用例与需求检索。实验表明，如bge-base-en-v1.5等微调后的紧凑模型，在准确率、延迟和成本之间取得更优平衡，挑战了“越大越好”的认知。A/B用户对比研究进一步证实，增强型助手在感知帮助性、真实性和满意度上优于通用大模型。这些发现为工业级HIL环境中部署高效、领域对齐的LLM助手提供了实践指导。
原文摘要 · Abstract (English)
Hardware-in-the-Loop (HIL) testing is essential for automotive validation but suffers from fragmented and underutilized test artifacts. This paper presents HIL-GPT, a retrieval-augmented generation (RAG) system integrating domain-adapted large language models (LLMs) with semantic retrieval. HIL-GPT leverages embedding fine-tuning using a domain-specific dataset constructed via heuristic mining and LLM-assisted synthesis, combined with vector indexing for scalable, traceable test case and requirement retrieval. Experiments show that fine-tuned compact models, such as \texttt{bge-base-en-v1.5}, achieve a superior trade-off between accuracy, latency, and cost compared to larger models, challenging the notion that bigger is always better. An A/B user study further confirms that RAG-enhanced assistants improve perceived helpfulness, truthfulness, and satisfaction over general-purpose LLMs. These findings provide insights for deploying efficient, domain-aligned LLM-based assistants in industrial HIL environments.
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