mlsanityData to 7 Oct 2026

A Survey on Out-of-Distribution Evaluation of Neural NLP Models

arXiv 2306.15261 ↗First posted 27 Jun 2023cs.CLcited by 20

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Abstract (arXiv)

Adversarial robustness, domain generalization and dataset biases are three active lines of research contributing to out-of-distribution (OOD) evaluation on neural NLP models. However, a comprehensive, integrated discussion of the three research lines is still lacking in the literature. In this survey, we 1) compare the three lines of research under a unifying definition; 2) summarize the data-generating processes and evaluation protocols for each line of research; and 3) emphasize the challenges and opportunities for future work.

Key facts. Cited by 20 papers in the map (data through 7 Oct 2026). Builds on 44 of its 53 references that are in the map. First posted on arXiv 27 Jun 2023.

Builds on

44 of 53 references.

References · 44

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  2. 2020Don't Stop Pretraining: Adapt Language Models to Domains and Taskscited by 1,548
  3. 2019Adversarial Examples Are Not Bugs, They Are Featurescited by 950
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32 more
  1. 2019Learning the Difference that Makes a Difference with Counterfactually-Augmented Datacited by 428
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  3. 2019Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classificationcited by 396
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  6. 2018Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifierscited by 341
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Citing papers per year: 2023: 3; 2024: 9; 2025: 5; 2026: 3.

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  1. 2023A Survey on Evaluation of Large Language Modelscited by 1,227
  2. 2024Safeguarding Large Language Models: A Surveycited by 73
  3. 2024Typos that Broke the RAG's Back: Genetic Attack on RAG Pipeline by Simulating Documents in the Wild via Low-level Perturbationscited by 64
  4. 2023LatEval: An Interactive LLMs Evaluation Benchmark with Incomplete Information from Lateral Thinking Puzzlescited by 41
  5. 2024Examining the robustness of LLM evaluation to the distributional assumptions of benchmarkscited by 31
  6. 2023A Fast Optimization View: Reformulating Single Layer Attention in LLM Based on Tensor and SVM Trick, and Solving It in Matrix Multiplication Timecited by 30
  7. 2024A Survey on Evaluation of Out-of-Distribution Generalizationcited by 20
  8. 2024SelfPrompt: Autonomously Evaluating LLM Robustness via Domain-Constrained Knowledge Guidelines and Refined Adversarial Promptscited by 10
  9. 2026Quantization Undoes Alignment: Bias Emergence in Compressed LLMs Across Models and Precision Levelscited by 5
  10. 2024An Empirical Study on Large Language Models in Accuracy and Robustness under Chinese Industrial Scenarioscited by 3
  11. 2024KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphscited by 3
  12. 2025Solving Situation Puzzles with Large Language Model and External Reformulationcited by 3
8 more
  1. 2025FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluationcited by 3
  2. 2024From Cool Demos to Production-Ready FMware: Core Challenges and a Technology Roadmapcited by 2
  3. 2024Assessing biomedical knowledge robustness in large language models by query-efficient sampling attackscited by 1
  4. 2025BroadGen: A Framework for Generating Effective and Efficient Advertiser Broad Match Keyphrase Recommendationscited by 1
  5. 2025POLIS-Bench: Towards Multi-Dimensional Evaluation of LLMs for Bilingual Policy Tasks in Governmental Scenarioscited by 1
  6. 2026SuperValid: Capability-Aligned OOD Validation for Generalizable Downstream Scalingcited by 1
  7. 2026What Current AI Benchmarks Leave Unmeasured: Modality, Search, Citations, and Implications (for Safety Evaluations)cited by 1
  8. 2025A Perplexity and Menger Curvature-Based Approach for Similarity Evaluation of Large Language Modelscited by 0