Exploring The Landscape of Distributional Robustness for Question Answering Models (2022)

Abstract: We conduct a large empirical evaluation to investigate the landscape of distributional robustness in question answering. Our investigation spans over 350 models and 16 question answering datasets, including a diverse set of architectures, model sizes, and adaptation methods (e.g., fine-tuning, adapter tuning, in-context learning, etc.). We find that, in many cases, model variations do not affect robustness and in-distribution performance alone determines out-of-distribution performance.Moreover, our findings indicate thati) zero-shot and in-context learning methods are more robust to distribution shifts than fully fine-tuned models;ii) few-shot prompt fine-tuned models exhibit better robustness than few-shot fine-tuned span prediction models;iii) parameter-efficient and robustness enhancing training methods provide no significant robustness improvements.In addition, we publicly release all evaluations to encourage researchers to further analyze robustness trends for question answering models.

## introduction over the past few years, natural language processing has seen substantial progress. in many benchmarks, large pre-trained models adapted to a target dataset reach or even surpass human performance @xcite @xcite @xcite . at the same time, current methods still fail to generalize reliably in a variety of test conditions @xcite @xcite @xcite , which limits their applicability and raises questions about what exactly the methods learn @xcite . one limitation of current benchmarks is that they often measure performance only on data that comes from the same distribution as the training set 1 university of washington. 2 allen institute for ai. correspondence to anasa2@cs.washington.edu. @xcite . however, evaluating models on a single test set provides no information on whether a method also performs well under distribution shift. while there is an increasing amount of research on robustness in nlp @xcite @xcite @xcite @xcite , the community has not yet adopted a common set of best practices for evaluating robustness. as a result, new methods often do not evaluate on comparable or even any robustness test sets, which makes it challenging to understand which methods generalize more reliably and whether nlp is making progress on robustness to distribution shift. to address this challenge and shed light on the robustness landscape in nlp, we conduct a large empirical evaluation of distributional robustness in question answering (qa). building on recent research on robustness in computer vision @xcite , we focus on distribution shifts that arise between two related but different test sets. these distribution shifts are sometimes called dataset shift to distinguish them from other kinds of distribution shift. an example of dataset shift is a pair of qa test sets where one test set is constructed from wikipedia articles and the other from amazon product reviews, possibly also with a different crowdsourcing process. in contrast to other notions of robustness such as adversarial robustness, dataset shifts involve no synthetic perturbations of existing test examples and are therefore more representative of generalization challenges arising "in the wild" @xcite . within the scope of dataset shifts for qa, our robustness evaluation includes a wide range of models and distribution shifts. specifically, we assembled a testbed of over 350 qa models and 16 qa datasets, including squad v1.1 @xcite , squadshifts @xcite , and roberta large t5 large opt 175b (4 examlpes) gpt-j gpt-2 xl (16 examples) spanbert base (512 examples) figure 1 : we evaluate over 350 models on 16 datasets to characterize the landscape of distributional robustness in question answering. our results span a variety of architectures and adaptation strategies, including zero-shot inference, fine-tuning, and incontext learning (icl). the x-axis shows performance on squad (in-distribution), while the y-axis shows the average performance on the 15 other qa datasets (out-ofdistribution). almost all models lie under the @xmath0, i.e., performance drops under distribution shift. moreover, within certain groups of models-for instance, icl models-in-distribution performance accurately predicts out-of-distribution performance. as in @xcite , we apply logit axis scaling to clarify that the relationship between in-distribution and out-ofdistribution performance is approximately linear in the logit domain. mrqa test sets @xcite . our testbed spans different model architectures, model sizes, and pre-training setups. in addition, we evaluate a variety of approaches for applying pre-trained models to question answering including supervised fine-tuning, in-context learning, parameter-efficient fine-tuning, zero-shot inference, and more. finally, we also include methods specifically designed to enhance robustness such as rxf @xcite and freelb @xcite . our testbed enables us to both identify overarching trends spanning many models, and to contextualize the robustness behavior of individual models. among our findings are the following key results: improving in-distribution performance usually also increases out-ofdistribution performance in a predictable way. figure 1 summarizes our findings and shows the average f1 score on all distribution shifts as a function of the f1 score on squad. interestingly, our overall results are analogous to similar large-scale robustness evaluations in computer vision @xcite @xcite , which suggests that there may be a shared underlying mechanism behind these distribution shifts that warrants further investigation. we hope that our work helps clarify the state of robustness in nlp and provides a starting point for future work. to simplify measuring robustness to dataset shift and enable future robustness improvements, we will release our testbed including all 350+ models and evaluation results. the remainder of the paper is organized as follows: first, we detail background and experimental setup ( §2). next, we introduce and answer our specific research questions ( §3, 4). finally, we discuss the limitations of our approach, overall conclusions, and directions for future investigation ( §6, 8). ## experimental setup our testbed includes over 350 models, covering a broad range of model architectures, pre-training datasets, and adaptation strategies. we use squad v1.1 @xcite as our reference point for question answering performance because squad is a popular dataset and the performance ceiling is comparatively well understood since humans can achieve an f1 score around 95 @xcite . for all models except those performing zero-shot inference, we adapt the models to question answering with the squad training set. we evaluate robustness to distribution shift on the remaining 15 question answering datasets ( ## results this section aims to answer our main research questions: figure 3 : encoder-only, encoder-decoder, and decoderonly models are equally as robust when fine-tuned by adding a span prediction head. we conclude that architecture does not determine distributional robustness. ## discussion this section discusses the aforementioned findings. in particular we discuss how the findings compare to analogous studies in vision, and how individual distribution shifts differ from aggregate trends. ## related work understanding how models behave under conditions that differ from training has been the subject of much attention both in natural language processing @xcite @xcite @xcite @xcite and computer vision @xcite @xcite . as in @xcite , we distinguish between synthetic and natural distribution shifts. the former includes any artificial perturbations to inputs, including adversarial attacks @xcite @xcite @xcite @xcite @xcite . in contrast, the later relates to naturally occurring data, without synthetic or adversarial perturbations. our work focuses on natural distribution shifts. most similar to our work is that of yogatama et al. (2019); talmor and berant (2019); sen and saffari (2020); fisch et al. (2019) and miller et al. (2020) , who examine the performance of models on multiple question answering datasets. our work provides a more comprehensive modeling survey, evaluating a broader set of models, adaptation strategies and datasets. in contrast to previous work, we evaluate zero-shot inference, in-context learning, few-shot fine-tuning, and parameter-efficient adaptation methods, which have only recently been popularized. finally, a variety of methods for improving robustness have been explored by previous work @xcite @xcite @xcite . instead of proposing methods to build more robust models, our goal is to empirically examine the landscape of robustness. as part of this goal, we evaluate robustness enhancing methods, in addition to other adaptation strategies. concurrent work by @xcite examines the robustness of few-shot fine-tuned models. they find that these models yield no additional robustness which matches the findings from our evaluation. ## conclusion we conduct an extensive evaluation of the robustness of different model and adaptation methods on 15 distribution shifts in question answering. our in-depth analysis suggests several concrete directions for future work: improving the in-distribution performance of icl methods and understanding why different few-shot fine-tuning methods yield 5978 7 17 27 37 47 57 67 77 87 97 f1 score on squad 8 18 28 38 48 58 68 78 88 f1 score on squadshifts_new_wiki squad squadshifts_new_wiki shift (a) squadshifts wikipedia 7 17 27 37 47 57 67 77 87 f1 score on squad 5 15 25 35 45 55 65 f1 score on searchqa squad searchqa shift (b) searchqa 7 17 27 37 47 57 67 77 87 f1 score on squad 4 14 24 34 44 54 64 f1 score on drop squad drop shift (c) drop @xmath1, models lie closer to the @xmath2. (middle) progress on squad is a weaker indicator for progress on searchqa for fully fine-tuned models and few-shot fine-tuned models. we find that zero-shot and icl models are less robust than fine-tuned and few-shot models with the exception of larger language models. (right) on the @xmath3, we observe that progress beyond 70 f1 on squad yields quick progress on drop for fine-tuned models.