all AI news
Honest Students from Untrusted Teachers: Learning an Interpretable Question-Answering Pipeline from a Pretrained Language Model. (arXiv:2210.02498v1 [cs.CL])
Oct. 7, 2022, 1:16 a.m. | Jacob Eisenstein, Daniel Andor, Bernd Bohnet, Michael Collins, David Mimno
cs.CL updates on arXiv.org arxiv.org
Explainable question answering systems should produce not only accurate
answers but also rationales that justify their reasoning and allow humans to
check their work. But what sorts of rationales are useful and how can we train
systems to produce them? We propose a new style of rationale for open-book
question answering, called \emph{markup-and-mask}, which combines aspects of
extractive and free-text explanations. In the markup phase, the passage is
augmented with free-text markup that enables each sentence to stand on its …
arxiv language language model pipeline pretrained language model students
More from arxiv.org / cs.CL updates on arXiv.org
Jobs in AI, ML, Big Data
AI Research Scientist
@ Vara | Berlin, Germany and Remote
Data Architect
@ University of Texas at Austin | Austin, TX
Data ETL Engineer
@ University of Texas at Austin | Austin, TX
Lead GNSS Data Scientist
@ Lurra Systems | Melbourne
Senior Data Engineer (m/f/d)
@ Project A Ventures | Berlin, Germany
Principle Research Scientist
@ Analog Devices | US, MA, Boston