Question answering over heterogeneous data requires reasoning over diverse sources of data, which is challenging due to the large scale of information and organic coupling of heterogeneous data. Various approaches have been proposed to address these challenges. One approach involves training specialized retrievers to select relevant information, thereby reducing the input length. Another approach is to transform diverse modalities of data into a single modality, simplifying the task difficulty and enabling more straightforward processing. In this paper, we propose HProPro, a novel program-based prompting framework for the hybrid question answering task. HProPro follows the code generation and execution paradigm. In addition, HProPro integrates various functions to tackle the hybrid reasoning scenario. Specifically, HProPro contains function declaration and function implementation to perform hybrid information-seeking over data from various sources and modalities, which enables reasoning over such data without training specialized retrievers or performing modal transformations. Experimental results on two typical hybrid question answering benchmarks HybridQA and MultiModalQA demonstrate the effectiveness of HProPro: it surpasses all baseline systems and achieves the best performances in the few-shot settings on both datasets.

翻译：混合数据上的问答需要对多种数据来源进行推理，由于信息规模庞大以及异构数据的有机耦合，这一任务极具挑战性。为解决这些难题，研究者提出了多种方法。一种方法是训练专门的检索器以选取相关信息，从而缩短输入长度；另一种方法是将不同模态的数据转换为单一模态，简化任务难度并实现更直接的处理。本文提出HProPro，一种新颖的基于编程式提示的混合问答框架。HProPro遵循代码生成与执行范式，并集成了多种函数以应对混合推理场景。具体而言，HProPro包含函数声明与函数实现，用于对来自不同来源与模态的数据进行混合信息搜索，从而无需训练专门的检索器或进行模态转换即可实现此类数据上的推理。在HybridQA和MultiModalQA这两个典型混合问答基准上的实验结果表明了HProPro的有效性：它在两个数据集的少样本设置下均超越所有基线系统，取得了最佳性能。