Large Language Models (LLMs) have shown remarkable effectiveness in various general-domain natural language processing (NLP) tasks. However, their performance in transportation safety domain tasks has been suboptimal, primarily attributed to the requirement for specialized transportation safety expertise in generating accurate responses [1]. To address this challenge, we introduce TrafficSafetyGPT, a novel LLAMA-based model, which has undergone supervised fine-tuning using TrafficSafety-2K dataset which has human labels from government produced guiding books and ChatGPT-generated instruction-output pairs. Our proposed TrafficSafetyGPT model and TrafficSafety-2K train dataset are accessible at https://github.com/ozheng1993/TrafficSafetyGPT.

翻译：大型语言模型（LLMs）已在多种通用自然语言处理（NLP）任务中展现出显著效果。然而，其在交通安全领域任务中的表现尚不理想，主要原因是生成准确响应需要专业的交通安全领域知识[1]。为解决这一挑战，我们提出了TrafficSafetyGPT——一种基于LLAMA的新型模型。该模型通过TrafficSafety-2K数据集进行了监督微调，该数据集包含从政府指导手册中获取的人工标注结果以及由ChatGPT生成的指令-输出对。我们提出的TrafficSafetyGPT模型及TrafficSafety-2K训练数据集可在https://github.com/ozheng1993/TrafficSafetyGPT获取。