On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study

• URL: http://arxiv.org/abs/2304.08653v1
• Date: Mon, 17 Apr 2023 23:06:28 GMT
• Title: On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study
• Authors: Polina Zablotskaia, Du Phan, Joshua Maynez, Shashi Narayan, Jie Ren, Jeremiah Liu
• Abstract summary: Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty. This means that they assign high confidence to low-quality predictions, leading to compromised reliability and trustworthiness in real-world applications. Probabilistic deep learning methods are common solutions to the miscalibration problem, but their relative effectiveness in complex autoregressive summarization tasks are not well-understood.
• Score: 14.041071717005362
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Modern deep models for summarization attains impressive benchmark performance, but they are prone to generating miscalibrated predictive uncertainty. This means that they assign high confidence to low-quality predictions, leading to compromised reliability and trustworthiness in real-world applications. Probabilistic deep learning methods are common solutions to the miscalibration problem. However, their relative effectiveness in complex autoregressive summarization tasks are not well-understood. In this work, we thoroughly investigate different state-of-the-art probabilistic methods' effectiveness in improving the uncertainty quality of the neural summarization models, across three large-scale benchmarks with varying difficulty. We show that the probabilistic methods consistently improve the model's generation and uncertainty quality, leading to improved selective generation performance (i.e., abstaining from low-quality summaries) in practice. We also reveal notable failure patterns of probabilistic methods widely-adopted in NLP community (e.g., Deep Ensemble and Monte Carlo Dropout), cautioning the importance of choosing appropriate method for the data setting.

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