基于检索引导的自动提示法用于物流帧检测
Auto-Prompting with Retrieval Guidance for Frame Detection in Logistics
AI总结：
本文提出基于检索引导的自动提示法，通过优化提示提升物流文本帧检测的准确性和效率。
摘要：
提示工程在适应大型语言模型（LLMs）进行复杂推理和标注任务中起着关键作用，无需大量微调。在本文中，我们提出了一种新颖的提示优化流程，用于物流文本中的帧检测，结合了检索增强生成（RAG）、少样本提示、推理链（CoT）推理以及自动CoT合成（Auto-CoT）来生成高效的任务特定提示。我们的方法核心是一个基于LLM的提示优化代理，通过检索示例、性能反馈和内部自我评估迭代地优化提示。我们的框架在一项真实的物流文本标注任务中进行了评估，其中推理准确性和标注效率是关键因素。实验结果表明，优化后的提示，特别是通过Auto-CoT和RAG增强的提示，与基线零样本或静态提示相比，将实际推理准确率提高了高达15%。该系统在多种LLM上均表现出一致的改进，包括GPT-4o、Qwen 2.5（72B）和LLaMA 3.1（70B），验证了其通用性和实用性。这些发现表明，结构化的提示优化是完全微调的一种可行替代方案，为在领域特定的NLP应用中部署LLM提供了可扩展的解决方案。
英文摘要：
Prompt engineering plays a critical role in adapting large language models (LLMs) to complex reasoning and labeling tasks without the need for extensive fine-tuning. In this paper, we propose a novel prompt optimization pipeline for frame detection in logistics texts, combining retrieval-augmented generation (RAG), few-shot prompting, chain-of-thought (CoT) reasoning, and automatic CoT synthesis (Auto-CoT) to generate highly effective task-specific prompts. Central to our approach is an LLM-based prompt optimizer agent that iteratively refines the prompts using retrieved examples, performance feedback, and internal self-evaluation. Our framework is evaluated on a real-world logistics text annotation task, where reasoning accuracy and labeling efficiency are critical. Experimental results show that the optimized prompts - particularly those enhanced via Auto-CoT and RAG - improve real-world inference accuracy by up to 15% compared to baseline zero-shot or static prompts. The system demonstrates consistent improvements across multiple LLMs, including GPT-4o, Qwen 2.5 (72B), and LLaMA 3.1 (70B), validating its generalizability and practical value. These findings suggest that structured prompt optimization is a viable alternative to full fine-tuning, offering scalable solutions for deploying LLMs in domain-specific NLP applications such as logistics.
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。
