Jihun Yun, Aurelie Lozano, et al.
NeurIPS 2021
The field of Question Answering (QA) has made remarkable progress in recent years, thanks to the advent of large pre-trained language models, newer realistic benchmark datasets with leaderboards, and novel algorithms for key components such as retrievers and readers. In this paper, we introduce PRIMEQA: a one-stop and open-source QA repository with an aim to democratize QA re-search and facilitate easy replication of state-of-the-art (SOTA) QA methods. PRIMEQA supports core QA functionalities like retrieval and reading comprehension as well as auxiliary capabilities such as question this http URL has been designed as an end-to-end toolkit for various use cases: building front-end applications, replicating SOTA methods on public benchmarks, and expanding pre-existing methods
Jihun Yun, Aurelie Lozano, et al.
NeurIPS 2021
Pin-Yu Chen, Cho-Jui Hsieh, et al.
KDD 2022
Trang H. Tran, Katya Scheinberg, et al.
ICML 2022
Hiroki Yanagisawa, Kohei Miyaguchi, et al.
NeurIPS 2022