Less Likely Brainstorming: Using Language Models to Generate Alternative
  Hypotheses
Liyan Tang Yifan Peng Yanshan Wang Ying Ding Greg Durrett Justin F. Rousseau
A human decision-maker benefits the most from an AI assistant that corrects
for their biases. For problems such as generating interpretation of a radiology
report given findings, a system predicting only highly likely outcomes may be
less useful, where such outcomes are already obvious to the user. To alleviate
biases in human decision-making, it is worth considering a broad differential
diagnosis, going beyond the most likely options. We introduce a new task, "less
likely brainstorming," that asks a model to generate outputs that humans think
are relevant but less likely to happen. We explore the task in two settings: a
brain MRI interpretation generation setting and an everyday commonsense
reasoning setting. We found that a baseline approach of training with less
likely hypotheses as targets generates outputs that humans evaluate as either
likely or irrelevant nearly half of the time; standard MLE training is not
effective. To tackle this problem, we propose a controlled text generation
method that uses a novel contrastive learning strategy to encourage models to
differentiate between generating likely and less likely outputs according to
humans. We compare our method with several state-of-the-art controlled text
generation models via automatic and human evaluations and show that our models'
capability of generating less likely outputs is improved.