Exploring Hybrid Question Answering via Program-based Prompting
Qi Shi Han Cui Haofeng Wang Qingfu Zhu Wanxiang Che Ting Liu
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.