| 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. |
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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. |
Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness (2024.naacl-long)
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| 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 . |
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. |
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 . |
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. |
Measuring the Robustness of NLP Models to Domain Shifts (2024.findings-emnlp)
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Nitay Calderon, Naveh Porat, Eyal Ben-David, Alexander Chapanin, Zorik Gekhman, Nadav Oved, Vitaly Shalumov, Roi Reichart
| Challenge: | Existing research on domain robustness (DR) relies on the Source Drop (SD) but lacks a complementary metric, a new study finds . |
| Approach: | They propose to use the Target Drop (TD) to measure domain DR . they use a DR benchmark consisting of 7 diverse tasks to measure both metrics . |
| Outcome: | The proposed model types excel in-domain, but few-shot LLMs often surpass them cross-domain showing better robustness. |
Reliability Testing for Natural Language Processing Systems (2021.acl-long)
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| Challenge: | a lack of rigorous testing and ML implicit assumption of identical training and testing distributions may result in systems that discriminate against minorities. |
| Approach: | They argue that reliability testing is needed to address the issue of demographics . they argue that adversarial attacks can be reframed for this goal . |
| Outcome: | The proposed framework will enable rigorous and targeted testing and aid in the enactment and enforcement of industry standards. |
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). |
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. |
Evaluating Robustness to Input Perturbations for Neural Machine Translation (2020.acl-main)
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| Challenge: | Recent work has shown that Neural Machine Translation models are brittle to small perturbations in the input. |
| Approach: | They propose to use subword regularization to measure the relative degradation and changes in translation when perturbations are added to the input. |
| Outcome: | The proposed measures show that the models are more robust to perturbations when subword regularization methods are used. |