Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA (2023)

Abstract: Despite remarkable progress made in natural language processing, even the state-of-the-art models often make incorrect predictions. Such predictions hamper the reliability of systems and limit their widespread adoption in real-world applications. ‘Selective prediction’ partly addresses the above concern by enabling models to abstain from answering when their predictions are likely to be incorrect. While selective prediction is advantageous, it leaves us with a pertinent question ‘what to do after abstention’. To this end, we present an explorative study on ‘Post-Abstention’, a task that allows re-attempting the abstained instances with the aim of increasing **coverage** of the system without significantly sacrificing its **accuracy**. We first provide mathematical formulation of this task and then explore several methods to solve it. Comprehensive experiments on 11 QA datasets show that these methods lead to considerable risk improvements –performance metric of the Post-Abstention task– both in the in-domain and the out-of-domain settings. We also conduct a thorough analysis of these results which further leads to several interesting findings. Finally, we believe that our work will encourage and facilitate further research in this important area of addressing the reliability of NLP systems.

## introduction despite remarkable progress made in natural language processing (nlp), even the state-of-the-art systems often make incorrect predictions. this problem becomes worse when the inputs tend to diverge from the training data distribution @xcite @xcite . incorrect predictions hamper the reliability of systems and limit their widespread adoption in real-world applications. selective prediction partly addresses the above concern by enabling models to abstain from answering when their predictions are likely to be incorrect. by avoiding potentially incorrect predictions, it allows maintaining high task accuracy and thus improves the system's reliability. selective prediction has recently received considerable attention from the nlp community leading to development of several methods @xcite @xcite . while these contributions are important, selective prediction leaves us with a pertinent question: what to do after abstention? in this work, we address the above question and present an explorative study on 'post-abstention', a task that allows re-attempting the abstained instances with the aim of increasing coverage of the given selective prediction system without significantly sacrificing its accuracy. figure 1 illustrates the benefit of employing a post-abstention method; a model that achieves an accuracy of 70% is first enabled with the selective prediction ability that increases the accuracy to 85% but answers only 71% instances. then, a post-abstention method is employed (for the 29% abstained instances) that assists the system in answering 9% more instances raising the coverage to 80% without considerably dropping the overall accuracy. we note that this task allows re-attempting all the abstained instances but does not require the system to necessarily output predictions for all of them i.e. the system can abstain even after utilizing a post-abstention method (when it is not sufficiently confident even in its new prediction). this facet not only allows the system to maintain its performance but also provides opportunities of sequentially applying stronger post-abstention methods to reliably and optimally increase the coverage in stages. we provide mathematical formulation of the post-abstention task and explore several baseline methods to solve it (section 2). to evaluate the efficacy of these methods, we conduct comprehensive experiments with 11 question-answering datasets from mrqa shared task @xcite in figure 1 : illustrating the impact of employing a post-abstention method on top of selective prediction system. a regular model that has an accuracy of 70% (at coverage 100%) is first enabled with selective prediction ability that increases the accuracy to 85% but drops the coverage to 71%. then, on employing a post-abstention method to the abstained instances (remaining 29%), coverage increases to 80% without a considerable drop in overall accuracy. in summary, our contributions are as follows: ## post-abstention in this section, we first provide background for post-abstention (2.1) and then describe the task (2.2) and its approaches (2.3). ## analysis what is the distribution of model's original confidence on the instances that get answered after applying post-abstention method? in figure 5 , we show the distribution of model's original confidence on squad instances that get answered by retop at abstention threshold 0.5. green-colored bars represent the number of instances answered from each confidence bucket. we found that retop answers a large number of instances from the high 973 confidence buckets; however, instances from even low confidence buckets get answered. this can further be controlled using the weight parameter (α) in the overall confidence computation. how often do the system's predictions change after applying retop and what is its impact? retop can either boost the confidence of the top most prediction of the given model or can select a different answer by re-examining its top n predictions. in figure 6 , we specifically analyze the latter scenario i.e. the instances on which retop's prediction differs from the original model's prediction. at a threshold of 0.5, the original system abstains on 3411 squad instances and after applying retop, it answers 1110 of those instances. out of these 1110 instances, the retop changes the prediction on 186 instances. the original prediction is incorrect in more cases (99 vs 87) and after applying retop, the system gives 116 correct predictions and only 70 incorrect. this implies that by overriding the original system's prediction, re-top improves the system's accuracy. however, in some cases, it also changed a correct prediction to incorrect but such cases are lesser than the former. to what extent do the instances answered by different post-abstention methods overlap? in figure 7 , we demonstrate the venn diagram of squad instances answered by retop and ensembling (mean) approaches at abstention threshold 0.5. retop answers 1110 instances while ensembling answers 277 and there 127 common instances between the two approaches. this indicates that the two sets are not mutually exclusive i.e. there are some instances that get targeted by both the approaches; however, there are a significant number of instances that are not in the intersection. this result motivates studying composite or sequential application of different post-abstention methods to further improve the post-abstention performance. ## conclusion and discussion in this work, we formulated 'post-abstention', a task that allows re-attempting the abstained instances of the given selective prediction system with the aim of increasing its coverage without significantly sacrificing the accuracy. we also explored several baseline methods for this task. through comprehensive experiments on 11 qa datasets, we showed that these methods lead to considerable performance improvements in both in-domain and out-of-domain settings. we further performed a thorough analysis that resulted in several interesting findings. looking forward, we believe that our work opens up several avenues for new research, such as exploring test-time adaptation, knowledge hunting, and other human intervention techniques like asking clarification questions as post-abstention methods (discussed in appendix d). studying the impact of composite or sequential application of multiple post-abstention methods in another promising direction. furthermore, prior selective prediction methods can also be repurposed and explored for this task. we plan to pursue these crucial research directions in our future work. finally, we hope our work will encourage further research in this important area and facilitate the development of more reliable nlp systems.