Abstract:
Open-domain QA has arisen as a natural language processing research subject to answer users’ inquiries in natural language from vast unstructured text collections. Among the most important business areas, using a corpus of unstructured documents as a source of information to answer a question is just one of the many benefits of working in an open-domain setting, which also allows for access to external knowledge even if the information related to the question is not uniquely identified in the sources. Thus, any procedures involving extracting information and data from available online sources and text documents may be enhanced with the help of open-domain question-answering systems. In this paper, we propose a method for developing an open-domain question-answering system by retrieving documents from a knowledge corpus comprising external and internal documents in response to input query and then extracting the answer using an instruction fine-tuned language model, following a “zero-shot” approach. While conceptually simple, this approach can be used as a flexible framework to develop an open-domain question-answering system efficiently.