Benchmarking Long-tail Generalization with Likelihood Splits

Findings of the Association for Computational Linguistics: EACL 2023, pages 963–983 May 2-6, 2023 ©2023 Association for Computational Linguistics Benchmarking Long-tail Generalization with Likelihood Splits Ameya Godbole and Robin Jia University of Southern California {ameyagod,robinjia}@usc.edu Abstract In order to reliably process natural language, NLP systems must generalize to the long tail of rare utterances. We propose a method to create challenging benchmarks that require generaliz- ing to the tail of the distribution by re-splitting existing datasets. We create ‘Likelihood Splits’ where examples that are assigned lower likeli- hood by a pre-trained language model (LM) are placed in the test set, and more likely examples are in the training set. This simple approach can be customized to construct meaningful train- test splits for a wide range of tasks. Likelihood Splits surface more challenges than random splits: relative error rates of state-of-the-art models increase by 59% for semantic parsing on SPIDER, 93% for natural language inference on SNLI, and 33% for yes/no question answer- ing on BOOLQ, on our splits compared with the corresponding random splits. Moreover, Like- lihood Splits create fairer benchmarks than ad- versarial filtering; when the LM used to create the splits is also employed as the task model, our splits do not unfairly penalize the LM. 1 Introduction Success on in-distribution test data does not neces- sarily show that a system has solved the underlying task at hand. Systems can achieve artificially high accuracy by exploiting dataset-specific shortcuts, such as spurious feature-label correlations that hold in the data but not in general (Gardner et al., 2021). In many datasets, a large proportion of test exam- ples are similar to training examples, further in- flating in-distribution accuracy (Lewis et al., 2021; Czarnowska et al., 2019; Orr et al., 2021). Out- of-distribution (OOD) evaluation paints a clearer picture of a system’s ability to perform the task. Prior work has proposed a variety of methods to test OOD generalization, each with their own strengths and weaknesses. Task-specific behav- ior tests (Ribeiro et al., 2020; Naik et al., 2018; Gardner et al., 2020) give insights into model be- Figure 1: Likelihood Splits: We propose to partition the dataset based on likelihood under a language model. The high-likelihood “head” of the distribution becomes the training set while we evaluate generalization to the low-likelihood “tail” of the data. Shown here are queries from the SPIDER dataset in different likelihood buckets: one possible tail generalization could be the handling uncommon entities with known query types. havior but require significant manual (often expert) effort to create. Adversarial data collection, in which annotators try to fool high-performing mod- els (Nie et al., 2020; Potts et al., 2021), also col- lects challenging examples, but runs the risk of focusing only on a narrow subset of model weak- nesses (Bowman and Dahl, 2021; Kaushik et al., 2021). Adversarial filtering removes easy exam- ples from existing datasets (Sakaguchi et al., 2021), but can disproportionately penalize the model used during filtering (Phang et al., 2021). Domain gener- alization tests transferability to …

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