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Supervised machine learning provides the learner with a set of input-outputexamples of the target task. Humans, however, can also learn to perform newtasks from instructions in natural language. Can machines learn to understandinstructions as well? We present the Turking Test, which examines a model'sability to follow natural language instructions of varying complexity. Theserange from simple tasks, like retrieving the nth word of a sentence, to onesthat require creativity, such as generating examples for SNLI and SQuAD inplace of human intelligence workers ("turkers"). Despite our lenient evaluationmethodology, we observe that a large pretrained language model performs poorlyacross all tasks. Analyzing the model's error patterns reveals that the modeltends to ignore explicit instructions and often generates outputs that cannotbe construed as an attempt to solve the task. While it is not yet clear whetherinstruction understanding can be captured by traditional language models, thesheer expressivity of instruction understanding makes it an appealingalternative to the rising few-shot inference paradigm.

The Turking Test: Can Language Models Understand Instructions?