mlsanityData to 7 Oct 2026

Reliability Testing for Natural Language Processing Systems

arXiv 2105.02590 ↗First posted 6 May 2021cs.LG cs.AI cs.CLcited by 25

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Abstract (arXiv)

Questions of fairness, robustness, and transparency are paramount to address before deploying NLP systems. Central to these concerns is the question of reliability: Can NLP systems reliably treat different demographics fairly and function correctly in diverse and noisy environments? To address this, we argue for the need for reliability testing and contextualize it among existing work on improving accountability. We show how adversarial attacks can be reframed for this goal, via a framework for developing reliability tests. We argue that reliability testing -- with an emphasis on interdisciplinary collaboration -- will enable rigorous and targeted testing, and aid in the enactment and enforcement of industry standards.

Key facts. Cited by 25 papers in the map (data through 7 Oct 2026). Builds on 53 of its 129 references that are in the map. First posted on arXiv 6 May 2021.

Builds on

53 of 129 references.

References · 53

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38 more
  1. 2019Universal Adversarial Triggers for Attacking and Analyzing NLPcited by 709
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  16. 2020A Survey of the State of Explainable AI for Natural Language Processingcited by 206
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Citing papers per year: 2021: 5; 2022: 6; 2023: 3; 2024: 7; 2025: 4.

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  1. 2022Prompting GPT-3 To Be Reliablecited by 258
  2. 2021Measure and Improve Robustness in NLP Models: A Surveycited by 107
  3. 2024On the Resilience of LLM-Based Multi-Agent Collaboration with Faulty Agentscited by 92
  4. 2024Red-Teaming for Generative AI: Silver Bullet or Security Theater?cited by 81
  5. 2021NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentationcited by 72
  6. 2022Characteristics of Harmful Text: Towards Rigorous Benchmarking of Language Modelscited by 49
  7. 2021Automatic Construction of Evaluation Suites for Natural Language Generation Datasetscited by 18
  8. 2022The Birth of Bias: A case study on the evolution of gender bias in an English language modelcited by 16
  9. 2023"One-Size-Fits-All"? Examining Expectations around What Constitute "Fair" or "Good" NLG System Behaviorscited by 11
  10. 2024Chaos with Keywords: Exposing Large Language Models Sycophantic Hallucination to Misleading Keywords and Evaluating Defense Strategiescited by 10
  11. 2025TAI3: Testing Agent Integrity in Interpreting User Intentcited by 7
  12. 2023LEAP: Efficient and Automated Test Method for NLP Softwarecited by 6
13 more
  1. 2022The Risks of Machine Learning Systemscited by 5
  2. 2021TraVLR: Now You See It, Now You Don't! A Bimodal Dataset for Evaluating Visio-Linguistic Reasoningcited by 4
  3. 2024MeTMaP: Metamorphic Testing for Detecting False Vector Matching Problems in LLM Augmented Generationcited by 4
  4. 2021Language Invariant Properties in Natural Language Processingcited by 3
  5. 2025Say It Another Way: Auditing LLMs with a User-Grounded Automated Paraphrasing Frameworkcited by 3
  6. 2024Risks and NLP Design: A Case Study on Procedural Document QAcited by 2
  7. 2022BotSIM: An End-to-End Bot Simulation Framework for Commercial Task-Oriented Dialog Systemscited by 1
  8. 2024Ethics Whitepaper: Whitepaper on Ethical Research into Large Language Modelscited by 1
  9. 2024Reliability of Topic Modelingcited by 1
  10. 2025Exploring Robustness of LLMs to Paraphrasing Based on Sociodemographic Factorscited by 1
  11. 2022BotSIM: An End-to-End Bot Simulation Toolkit for Commercial Task-Oriented Dialog Systemscited by 0
  12. 2023A Group-Specific Approach to NLP for Hate Speech Detectioncited by 0
  13. 2025AutoTestForge: A Multidimensional Automated Testing Framework for Natural Language Processing Modelscited by 0