对比四种提示策略，发现链式思维少样本提示最有效。
Evaluating Prompting Strategies for Chart Question Answering with Large Language Models
- 用四种提示方法在图表问答上测试大模型表现。
- 少样本链式思维提示准确率最高达78.2%。
- 适合需要复杂推理的结构化数据任务。
提示策略影响大模型的推理能力，但在基于图表的问答任务中仍缺乏深入研究。本文系统评估了四种常见提示范式（零样本、少样本、零样本思维链、少样本思维链）在GPT-3.5、GPT-4和GPT-4o上的表现，实验基于ChartQA数据集，共使用1,200个多样化样本。框架仅以结构化图表数据为输入，将提示结构作为唯一变量，采用准确率（Accuracy）与精确匹配（Exact Match）两项指标进行评估。结果显示，少样本思维链提示在推理密集型问题上表现最佳，准确率最高达78.2%；少样本提示更利于格式遵循；而零样本仅在高容量模型处理简单任务时表现良好。研究为结构化数据推理任务中的提示策略选择提供了可操作指导，对实际应用中的效率与准确性具有重要意义。
原文摘要 · Abstract (English)
Prompting strategies affect LLM reasoning performance, but their role in chart-based QA remains underexplored. We present a systematic evaluation of four widely used prompting paradigms (Zero-Shot, Few-Shot, Zero-Shot Chain-of-Thought, and Few-Shot Chain-of-Thought) across GPT-3.5, GPT-4, and GPT-4o on the ChartQA dataset. Our framework operates exclusively on structured chart data, isolating prompt structure as the only experimental variable, and evaluates performance using two metrics: Accuracy and Exact Match. Results from 1,200 diverse ChartQA samples show that Few-Shot Chain-of-Thought prompting consistently yields the highest accuracy (up to 78.2\%), particularly on reasoning-intensive questions, while Few-Shot prompting improves format adherence. Zero-Shot performs well only with high-capacity models on simpler tasks. These findings provide actionable guidance for selecting prompting strategies in structured data reasoning tasks, with implications for both efficiency and accuracy in real-world applications.
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