Can Out-of-Distribution Evaluations Uncover Reliance on Prediction Shortcuts? A Case Study in Question Answering (2025.findings-emnlp)
Copied to clipboard
| Challenge: | Existing work assesses models’ generalization capabilities through the lens of performance on out-of-distribution (OOD) datasets. |
| Approach: | They challenge this assumption by comparing OOD evaluations with failure modes documented in existing question-answering (QA) models. |
| Outcome: | The proposed evaluations show that the models' generalization capabilities are under-performing on out-of-distribution datasets, while others are underperforming on in-difference datasets. |
Similar Papers
Think Twice: Measuring the Efficiency of Eliminating Prediction Shortcuts of Question Answering Models (2024.eacl-long)
Copied to clipboard
| Challenge: | Existing work shows that Large Language Models (LLMs) are not robust to complex language understanding tasks due to reliance on spurious correlations of training datasets. |
| Approach: | They propose a method for measuring model reliance on spurious features by exploiting chosen biases on out-of-distribution (OOD) datasets. |
| Outcome: | The proposed method shows that the reported OOD gains of debiasing methods can't be explained by mitigated reliance on biased features, suggesting that biases are shared among different QA datasets. |
Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future (2023.emnlp-main)
Copied to clipboard
Linyi Yang, Yaoxian Song, Xuan Ren, Chenyang Lyu, Yidong Wang, Jingming Zhuo, Lingqiao Liu, Jindong Wang, Jennifer Foster, Yue Zhang
| Challenge: | Existing literature on the generalization of machine learning models to out-of-distribution data is lacking. |
| Approach: | They propose to present the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
| Outcome: | The proposed survey provides the first comprehensive review of recent progress, methods, and evaluations on the generalization challenge from an OOD perspective in natural language understanding. |
Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization (2023.findings-eacl)
Copied to clipboard
Aishwarya Agrawal, Ivana Kajic, Emanuele Bugliarello, Elnaz Davoodi, Anita Gergely, Phil Blunsom, Aida Nematzadeh
| Challenge: | Visual question answering (VQA) is a task of answering open-ended questions about images. |
| Approach: | They evaluate two vision-and-language (V&L) models under different settings . they find they tend to learn to solve the benchmark rather than the skills required by VQA . |
| Outcome: | The proposed models exhibit poor generalization under out-of-distribution settings. |
Pretrained Transformers Improve Out-of-Distribution Robustness (2020.acl-main)
Copied to clipboard
| Challenge: | Pretrained Transformers are more effective at detecting anomalous or OOD examples, while many previous models are frequently worse than chance. |
| Approach: | They construct a new robustness benchmark with real distribution shifts to measure out-of-distribution generalization for seven NLP datasets and compare them to previous models. |
| Outcome: | The proposed model generalizations for seven datasets show that pretrained Transformers are significantly less effective at detecting anomalous or OOD examples, while many previous models are often worse than chance. |
Do Generalisation Results Generalise? (2026.findings-acl)
Copied to clipboard
| Challenge: | Existing studies evaluating generalisation performance on large language models focuses on a single out-of-distribution dataset . |
| Approach: | They examine whether OOD generalisation results generalise across multiple OOD testsets throughout a finetuning run and then evaluate the partial correlation of results . |
| Outcome: | The proposed model achieves high scores on multiple OOD testsets, regressing out in-domain performance. |
GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective (2023.findings-acl)
Copied to clipboard
Linyi Yang, Shuibai Zhang, Libo Qin, Yafu Li, Yidong Wang, Hanmeng Liu, Jindong Wang, Xing Xie, Yue Zhang
| Challenge: | Pre-trained language models (PLMs) have improved generalization performance but the out-of-distribution (OOD) generalization problem remains a challenge in many NLP tasks. |
| Approach: | They propose to create a benchmark for evaluating out-of-distribution (OOD) generalization in NLP models. |
| Outcome: | The proposed benchmarks highlight the importance of OOD robustness and provide insights on how to measure it and improve it. |
Language Prior Is Not the Only Shortcut: A Benchmark for Shortcut Learning in VQA (2022.findings-emnlp)
Copied to clipboard
Qingyi Si, Fandong Meng, Mingyu Zheng, Zheng Lin, Yuanxin Liu, Peng Fu, Yanan Cao, Weiping Wang, Jie Zhou
| Challenge: | Visual Question Answering (VQA) models are prone to learn the shortcut solution formed by dataset biases rather than the intended solution. |
| Approach: | They propose a dataset that considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. |
| Outcome: | The proposed dataset considers varying types of shortcuts by constructing different distribution shifts in multiple OOD test sets. |
An Investigation of the (In)effectiveness of Counterfactually Augmented Data (2022.acl-long)
Copied to clipboard
| Challenge: | Pretrained language models tend to rely on spurious correlations and generalize poorly to out-of-distribution (OOD) data. |
| Approach: | They propose to use counterfactually-augmented data (CAD) to identify robust features that are invariant under distribution shift to train models for OOD generalization. |
| Outcome: | The proposed model can learn robust features that are invariant under distribution shifts, but lacks spurious correlations, and may exacerbate existing correlations. |
Guide the Learner: Controlling Product of Experts Debiasing Method Based on Token Attribution Similarities (2023.eacl-main)
Copied to clipboard
| Challenge: | Several proposals have been put forward for improving out-of-distribution performance by mitigating dataset biases. |
| Approach: | They propose a fine-tuning strategy that incorporates the similarity between the main and biased model attribution scores in a Product of Experts (PoE) loss function to further improve OOD performance. |
| Outcome: | The proposed method improves OOD performance while maintaining in-distribution performance. |
SimSCOOD: Systematic Analysis of Out-of-Distribution Generalization in Fine-tuned Source Code Models (2024.findings-naacl)
Copied to clipboard
| Challenge: | Large datasets are increasingly available for pre-training source code models, but obtaining representative training data that fully covers the code distribution for specific downstream tasks remains challenging due to the task-specific nature and limited labeling resources. |
| Approach: | They propose a systematic approach that simulates various OOD scenarios along different dimensions of source code data properties and investigates model behavior under different fine-tuning methodologies. |
| Outcome: | The proposed approach simulates various OOD scenarios along different dimensions of source code data properties and exposes multiple failure modes attributed to OOD generalization issues. |