Calibration of Machine Reading Systems at Scale
This addresses the issue of unreliable confidence measures in complex AI systems for users needing trustworthy predictions, though it is incremental as it builds on existing calibration approaches.
The paper tackled the problem of calibrating confidence estimates in open-domain machine reading systems like question answering and claim verification, showing that existing calibration techniques fail to scale to these complex settings, and proposed simple extensions that work well, enabling selective prediction for unanswerable or out-of-distribution questions.
In typical machine learning systems, an estimate of the probability of the prediction is used to assess the system's confidence in the prediction. This confidence measure is usually uncalibrated; i.e.\ the system's confidence in the prediction does not match the true probability of the predicted output. In this paper, we present an investigation into calibrating open setting machine reading systems such as open-domain question answering and claim verification systems. We show that calibrating such complex systems which contain discrete retrieval and deep reading components is challenging and current calibration techniques fail to scale to these settings. We propose simple extensions to existing calibration approaches that allows us to adapt them to these settings. Our experimental results reveal that the approach works well, and can be useful to selectively predict answers when question answering systems are posed with unanswerable or out-of-the-training distribution questions.