Exploring Hybrid Question Answering via Program-based Prompting (2024)

Abstract: 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.

## introduction question answering systems @xcite @xcite have attracted significant attention and made considerable progress in recent years. however, realworld data often exists in diverse formats and originates from multiple sources. consequently, re- ## conclusion in this work, we propose hpropro, a novel program-based prompting framework for hqa tasks, which does not require domain-specific retriever and modal transformation, but integrates various functions to interact with heterogeneous data instead. experimental results on two typical hqa benchmarks hybridqa and multimodalqa show the effectiveness of hpropro that hpro-pro achieves the best performances under the fewshot settings. for future work, we hope to further utilize the coding capabilities of the llms, allowing the model to judge and self-create more customized functions based on different scenarios.