Web: http://arxiv.org/abs/2110.08542

May 11, 2022, 1:11 a.m. | Tushar Khot, Kyle Richardson, Daniel Khashabi, Ashish Sabharwal

cs.CL updates on arXiv.org arxiv.org

Training giant models from scratch for each complex task is resource- and
data-inefficient. To help develop models that can leverage existing systems, we
propose a new challenge: Learning to solve complex tasks by communicating with
existing agents (or models) in natural language. We design a synthetic
benchmark, CommaQA, with three complex reasoning tasks (explicit, implicit,
numeric) designed to be solved by communicating with existing QA agents. For
instance, using text and table QA agents to answer questions such as "Who …

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