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Analysis Methods in Neural Language Processing: A Survey

arXiv 1812.08951 ↗First posted 21 Dec 2018cs.CL cs.LG cs.NEcited by 276

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Abstract (arXiv)

The field of natural language processing has seen impressive progress in recent years, with neural network models replacing many of the traditional systems. A plethora of new models have been proposed, many of which are thought to be opaque compared to their feature-rich counterparts. This has led researchers to analyze, interpret, and evaluate neural networks in novel and more fine-grained ways. In this survey paper, we review analysis methods in neural language processing, categorize them according to prominent research trends, highlight existing limitations, and point to potential directions for future work.

Key facts. Cited by 276 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Dec 2018.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 69; 2022: 47; 2023: 46; 2024: 43; 2025: 30; 2026: 41.

276 papers.

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  3. 2021Ethical and social risks of harm from Language Modelscited by 957
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  7. 2021Measuring and Improving Consistency in Pretrained Language Modelscited by 361
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  10. 2023Linearity of Relation Decoding in Transformer Language Modelscited by 190
  11. 2021Implicit Representations of Meaning in Neural Language Modelscited by 179
  12. 2021Alignment of Language Agentscited by 167
38 more
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  2. 2021Post-hoc Interpretability for Neural NLP: A Surveycited by 155
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  5. 2022Towards Faithful Model Explanation in NLP: A Surveycited by 129
  6. 2021Causal Analysis of Syntactic Agreement Mechanisms in Neural Language Modelscited by 127
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  8. 2023How do Language Models Bind Entities in Context?cited by 102
  9. 2023Tracr: Compiled Transformers as a Laboratory for Interpretabilitycited by 99
  10. 2022What Artificial Neural Networks Can Tell Us About Human Language Acquisitioncited by 97
  11. 2022Interpreting Language Models with Contrastive Explanationscited by 91
  12. 2023Attention Sorting Combats Recency Bias In Long Context Language Modelscited by 89
  13. 2024LLM Internal States Reveal Hallucination Risk Faced With a Querycited by 80
  14. 2023Inseq: An Interpretability Toolkit for Sequence Generation Modelscited by 78
  15. 2024Hopping Too Late: Exploring the Limitations of Large Language Models on Multi-Hop Queriescited by 78
  16. 2021Language Model Evaluation Beyond Perplexitycited by 77
  17. 2022A Survey of Knowledge Enhanced Pre-trained Language Modelscited by 76
  18. 2021Vision-and-Language or Vision-for-Language? On Cross-Modal Influence in Multimodal Transformerscited by 71
  19. 2023Can ChatGPT Replace Traditional KBQA Models? An In-depth Analysis of the Question Answering Performance of the GPT LLM Familycited by 71
  20. 2022Gradient-Based Constrained Sampling from Language Modelscited by 64
  21. 2023What Do Self-Supervised Speech Models Know About Words?cited by 62
  22. 2023Towards Vision-Language Mechanistic Interpretability: A Causal Tracing Tool for BLIPcited by 62
  23. 2021Conditional probing: measuring usable information beyond a baselinecited by 61
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  25. 2021ExplainaBoard: An Explainable Leaderboard for NLPcited by 50
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  27. 2022On the Blind Spots of Model-Based Evaluation Metrics for Text Generationcited by 48
  28. 2024Do language models plan ahead for future tokens?cited by 47
  29. 2025Detecting High-Stakes Interactions with Activation Probescited by 45
  30. 2021On the proper role of linguistically-oriented deep net analysis in linguistic theorizingcited by 43
  31. 2024Vision-Language Models Provide Promptable Representations for Reinforcement Learningcited by 41
  32. 2021BERTnesia: Investigating the capture and forgetting of knowledge in BERTcited by 40
  33. 2021The Low-Dimensional Linear Geometry of Contextualized Word Representationscited by 39
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  36. 2024Locate-then-edit for Multi-hop Factual Recall under Knowledge Editingcited by 36
  37. 2023In ChatGPT We Trust? Measuring and Characterizing the Reliability of ChatGPTcited by 35
  38. 2024Locating and Editing Factual Associations in Mambacited by 35

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