A Survey for Efficient Open Domain Question Answering
It is a survey paper that informs scholars about efficiency challenges in ODQA, which is incremental as it summarizes existing work rather than introducing new methods.
This paper surveys recent advances in efficient open domain question answering (ODQA), addressing the trade-off between accuracy, memory consumption, and processing speed in NLP models, and provides quantitative analysis on these metrics.
Open domain question answering (ODQA) is a longstanding task aimed at answering factual questions from a large knowledge corpus without any explicit evidence in natural language processing (NLP). Recent works have predominantly focused on improving the answering accuracy and achieved promising progress. However, higher accuracy often comes with more memory consumption and inference latency, which might not necessarily be efficient enough for direct deployment in the real world. Thus, a trade-off between accuracy, memory consumption and processing speed is pursued. In this paper, we provide a survey of recent advances in the efficiency of ODQA models. We walk through the ODQA models and conclude the core techniques on efficiency. Quantitative analysis on memory cost, processing speed, accuracy and overall comparison are given. We hope that this work would keep interested scholars informed of the advances and open challenges in ODQA efficiency research, and thus contribute to the further development of ODQA efficiency.