On Uncertainty Calibration and Selective Generation in Probabilistic Neural Summarization: A Benchmark Study (2023)

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 using our newly introduced evaluation protocol. 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.

## introduction in recent years, autoregressive deep models for text summarization have achieved impressive performance. however, despite their success, these models often suffer from a critical flaw: they generate prediction with high confidence even when the quality of the summary is low @xcite . this can severely compromise the reliability and trustworthiness of the generated summaries in realworld applications. in the probabilistic forecast literature, such issue is known under the term miscalibration, i.e., the model's predictive confidence * equal contribution. in the uncertainty literature, probabilistic deep learning has emerged as a principled approach to tackle model miscalibration while maintaining prediction quality @xcite . intuitively, probabilistic dnns generate multiple plausible predictions from its posterior predictive pm (y|x) = 1 m m @xmath0, thereby mitigating the overconfidence of the individual model prediction. although well-tested in classification tasks, the effectiveness of different state-of-art probabilistic methods in improving neural summarization models' uncertainty quality has been less explored. the existing study mostly focuses on a particular classic method (e.g., monte carlo dropout or mcd) and tested on relatively simple datasets that doesn't fully capture the realistic usage @xcite . in this work, we address this by conducting a comprehensive investigation of the relative effectiveness of state-of-the-art probabilistic methods in improving the uncertainty quality of neural summarization models. we interrogate both classic approaches such as monte carlo dropout (mcd) and deep ensemble (de), and more recent state-of-art methods such as batch ensemble (be) spectral-normalized gaussian process (sngp) and their combinations that address the latency and quality caveats of the classic methods @xcite @xcite . furthermore, we evaluate method performance across multiple benchmarks of varying difficulty to ensure the practical relevance of our result, and to uncover potential failure patterns of different approaches. our contributions are: ## related work probabilitistic learning for seq2seq models. developed primarily in the context of discriminative models, the state-of-art probabilistic approaches can be applied to large neural models without sacrificing performance @xcite @xcite . recently, however, initial investigations into unsupervised uncertainty estimation for structured prediction have appeared, primarily focusing on more basic approaches such as monte carlo dropout (mcd) or deep ensemble (de) @xcite @xcite @xcite , with a few work looking into summarization tasks @xcite . in comparison, this work focuses on an unbiased evaluation of a wide range of state-of-the-art probabilistic methods on tasks with varying difficulty, and reveals failure patterns of classic approaches such as mcd and de. calibration technique in language processing. @xcite proposed improving calibration of document classifier using of temperature scaling. @xcite and @xcite explored improving calibration in neural machine translation using label smoothing. @xcite noted that calibration methods can be used to improve the accuracy of pre-trained language models. @xcite proposed a novel training approach to improve calibration by minimizing a combined loss of cross-entropy and calibration. in the summarization literature, @xcite @xcite @xcite explored calibrating model probability using contrastive learning approaches. most of these techniques focus on deterministic models. they are orthogonal to and can be combined with the probabilistic approaches we explore in this work. ## methods probabilistic methods have been adopted to increase the reliability of pre-trained language models. plex paper @xcite provided a nice survey on the robustness of uncertainty methods on text classification tasks. in this study, we opted for the methods that are widely recognized and used. section a.7 contains in-depth details, here we provide a general overview: single-model methods: for all methods, we use the official base t5 checkpoint, which are pretrained on a large corpus like c4 @xcite . we then finetune the parameters on summarization tasks. to generate prediction from the model posterior in all, we perform beam inference with respect to the model's conditional posterior mean probability, i.e., p(y t |@xmath1, x) = 1 m m @xmath2, x), where @xmath3. to quantify model uncertainty, we consider the length-normalized predicted log-probabilities following previous work, i.e., u(y|x) := 1 t t @xmath4, x) @xcite where x is the input sequence, y is the output sequence, y t is the t-th token of y, and t is the length of the sequence, i.e. the number of tokens in y. ## experiments for the first time, we benchmark the probabilistic uncertainty calibration methods. we use our proposed evaluation protocol, consisting of assessing the rouge quality improvements, measuring uncertainty calibration, which includes our newly proposed sequence-level expected calibration error (ece), assessing rank correlation and finally analysing selective generation via abstention as a measure of language model calibration. we evaluate all methods on three datasets: xsum @xcite , cnn/dailymail @xcite and reddittifulong @xcite due to their diversity in abstractiveness, lengths, domain and style (see section a.6 for additional details). for all experiments we use beam search decoding, which was adapted to work with ensemble generation. we have also adapted sngp and be algorithms to work with the sequence generation and the corresponding loss (see section a.7 for more details). ## conclusion we have adapted the most popular probabilistic calibration methods to the plms use-case and have conducted a novel and extensive study of the calibration effect on plms uncertainty and summarization quality. we proposed a novel evaluation protocol for the uncertainty estimation in plms. we demonstrated that probabilistic calibration methods can have a positive impact on the quality of generated summaries and increase reliability of the models. our main findings can be summarized in two points: we hope to see our efforts making a significant contribution to the improvements of the reliability of plms, enabling future researchers to effectively leverage probabilistic calibration methods in the development and analysis of these models.