| Challenge: | Existing approaches to measure robustness are problematic, and out-of-domain evaluations are no longer relevant. |
| Approach: | They examine models of different sizes spanning different architectural choices and pretraining objectives. |
| Outcome: | The results show that not all out-of-domain tests provide insight into robustness . merely scaling models does not make them adequately robust . |
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Measure and Improve Robustness in NLP Models: A Survey (2022.naacl-main)
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| Challenge: | Despite the performance gains, NLP models are still fragile and brittle to out-of-domain data, adversarial attacks, or small perturbation to the input. |
| Approach: | They propose a survey of how to define, measure and improve robustness in NLP by connecting multiple definitions of robustness and identifying failures. |
| Outcome: | The proposed models are robust against unseen or challenging scenarios, but are still fragile and brittle to out-of-domain data and adversarial attacks. |
Robustness and Adversarial Examples in Natural Language Processing (2021.emnlp-tutorials)
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| Challenge: | This tutorial aims to raise awareness of practical concerns about NLP robustness . it aims at addressing the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
| Approach: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it reviews recent studies on analyzing the weakness of NLP systems when facing adversarial inputs . |
| Outcome: | This tutorial aims to bring awareness of practical concerns about NLP robustness . it will examine the weaknesses of NLP systems when faced with adversarial inputs and data with a distribution shift . |
A Novel Metric for Measuring the Robustness of Large Language Models in Non-adversarial Scenarios (2024.findings-emnlp)
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| Challenge: | Using large language models, we evaluated their robustness on multiple datasets. |
| Approach: | They propose a new metric for assessing model robustness by empirical evaluation of several models on multiple datasets. |
| Outcome: | The proposed metric is based on a set of datasets that are constructed by introducing naturally-occurring, non-malicious perturbations or by generating semantically equivalent paraphrases of input questions or statements. |
Interpreting the Robustness of Neural NLP Models to Textual Perturbations (2022.findings-acl)
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| Challenge: | Modern Natural Language Processing models are sensitive to input perturbations and their performance can decrease when applied to noisy data. |
| Approach: | They propose to explain the extent to which a model is affected by an unseen textual perturbation by the learnability of the perturbation. |
| Outcome: | The proposed model is better at identifying a perturbation (higher learnability) but worse at ignoring it (lower robustness). |
Methods for Estimating and Improving Robustness of Language Models (2022.naacl-srw)
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| Challenge: | Large language models suffer from weak generalisation ability due to shallow textual relations over full semantic complexity of the problem. |
| Approach: | They propose to incorporate some of these measures into training objectives to enhance distributional robustness of LLMs. |
| Outcome: | The proposed models outperform human models on complex tasks and outperformed other models on deep networks. |
Measuring Robustness for NLP (2022.coling-1)
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| Challenge: | Existing methods to evaluate NLP models are limited to news domains and cannot be generalized to other domains. |
| Approach: | They propose a measure of NLP quality based on robustness . they measure consistency of cross-domain accuracy and introduce coefficient of variation and gamma-Robustness based upon human evaluation . |
| Outcome: | The proposed approach shows higher agreement with human evaluation than accuracy scores on ranking machine translation systems. |
How Reliable are Model Diagnostics? (2021.findings-acl)
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| Challenge: | Contemporary statistical models trade off interpretability and simplicity for powerful parameterizations and inductive biases, enabling impressive performance. |
| Approach: | They examine three recent models and find they are not yet reliable . they also formulate recommendations for practitioners and researchers . |
| Outcome: | The proposed models are not as reliable as previously assumed, the authors argue . their findings suggest that they are needed for improving models and training setups . |
Robustness Gym: Unifying the NLP Evaluation Landscape (2021.naacl-demos)
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| Challenge: | Existing tools cater to specialized set of evaluations and provide no clear way to leverage or share findings from prior evaluations. |
| Approach: | They propose a toolkit that unifies 4 evaluation paradigms to provide a common platform for evaluation. |
| Outcome: | The proposed evaluation toolkit unifies 4 evaluation paradigms and is under active development. |
Are Sample-Efficient NLP Models More Robust? (2023.acl-short)
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| Challenge: | Recent work in image classification and extractive question answering have observed that pre-trained models trained on less in-distribution data have better out-of-distortion performance. |
| Approach: | They conduct a large empirical study to investigate the relationship between sample efficiency and robustness. |
| Outcome: | The results show that pre-trained models with lower sample efficiency perform better on some tasks but not others. |
SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models (2025.naacl-industry)
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| Challenge: | Typical evaluations of Large Language Models (LLMs) report a single accuracy metric per dataset, often derived from an optimized setup. |
| Approach: | They propose a framework for non-adversarial evaluation of large language models that evaluates models by repeatedly testing them on the same benchmarks in various setups. |
| Outcome: | The proposed framework evaluates models by repeatedly testing them on the same benchmarks in various setups to give a realistic estimate of their accuracy and consistency. |