评估大型语言模型在图表问答中的提示策略
Evaluating Prompting Strategies for Chart Question Answering with Large Language Models
AI总结：
本文系统评估了四种提示策略在图表问答任务中的表现，发现Few-Shot Chain-of-Thought策略在准确性和格式遵循上表现最佳，为结构化数据推理任务提供了选择提示策略的指导。
摘要：
提示策略影响LLM推理性能，但其在基于图表的问答中的作用仍待探索。本文系统评估了四种广泛使用的提示范式（零样本、少样本、零样本链式思维、少样本链式思维）在GPT-3.5、GPT-4和GPT-4o上的表现，基于ChartQA数据集。我们的框架仅操作于结构化图表数据，将提示结构作为唯一实验变量，并使用准确率和精确匹配两个指标评估性能。1,200个多样化的ChartQA样本结果显示，少样本链式思维提示策略在准确率（最高达78.2%）和推理密集型问题上表现最佳，而少样本提示策略提高了格式遵循性。零样本仅在高容量模型上对简单任务表现良好。这些发现为选择结构化数据推理任务的提示策略提供了可行指导，对实际应用中的效率和准确性具有启示意义。
英文摘要：
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.
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。
