AI2 releases demo of question-answering model it claims outperforms GPT-3
From the beginning of the digital age, we’ve looked to our computers for answers. Nowhere is this so evident as in the computer science discipline known as question answering, or QA. Overlapping the fields of natural language processing and information retrieval, QA initially utilized handcrafted knowledge bases to answer questions. Today, however, these systems increasingly use machine learning and pre-trained language models like OpenAI’s GPT-3 to achieve their results.
One of the newest and most innovative of these QA models has recently been developed at the Allen Institute for AI (AI2) in Seattle. Macaw, which loosely stands for “Multi-angle c(q)uestion answering,” was developed as an open-source project and is available to the community via GitHub.
If you’d like to see how Macaw works, AI2 is making their interactive demo available to the public starting today. You can use the demo to explore Macaw’s answers and compare them to those given by the GPT-3 language model on a benchmark set of questions.
