mlsanityData to 7 Oct 2026

Analyzing and Interpreting Neural Networks for NLP: A Report on the First BlackboxNLP Workshop

arXiv 1904.04063 ↗First posted 5 Apr 2019cs.CL stat.MLcited by 22

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Abstract (arXiv)

The EMNLP 2018 workshop BlackboxNLP was dedicated to resources and techniques specifically developed for analyzing and understanding the inner-workings and representations acquired by neural models of language. Approaches included: systematic manipulation of input to neural networks and investigating the impact on their performance, testing whether interpretable knowledge can be decoded from intermediate representations acquired by neural networks, proposing modifications to neural network architectures to make their knowledge state or generated output more explainable, and examining the performance of networks on simplified or formal languages. Here we review a number of representative studies in each category.

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Apr 2019.

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Citing papers per year: 2021: 3; 2022: 2; 2023: 4; 2024: 8; 2025: 4; 2026: 1.

22 papers.

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  1. 2023Towards Automated Circuit Discovery for Mechanistic Interpretabilitycited by 700
  2. 2022minicons: Enabling Flexible Behavioral and Representational Analyses of Transformer Language Modelscited by 58
  3. 2021Structural Persistence in Language Models: Priming as a Window into Abstract Language Representationscited by 44
  4. 2025Evaluating and Steering Modality Preferences in Multimodal Large Language Modelcited by 33
  5. 2021Language Models Use Monotonicity to Assess NPI Licensingcited by 30
  6. 2024Transferable Embedding Inversion Attack: Uncovering Privacy Risks in Text Embeddings without Model Queriescited by 25
  7. 2024Mechanistic?cited by 25
  8. 2022Language model acceptability judgements are not always robust to contextcited by 22
  9. 2024Transformer Circuit Faithfulness Metrics are not Robustcited by 18
  10. 2025When Modalities Conflict: How Unimodal Reasoning Uncertainty Governs Preference Dynamics in MLLMscited by 18
  11. 2025Explainability in Practice: A Survey of Explainable NLP Across Various Domainscited by 11
  12. 2024Potential and Limitations of LLMs in Capturing Structured Semantics: A Case Study on SRLcited by 10
10 more
  1. 2024Phase Transitions in the Output Distribution of Large Language Modelscited by 7
  2. 2024Sparse Attention Decomposition Applied to Circuit Tracingcited by 4
  3. 2025Discovering Transformer Circuits via a Hybrid Attribution and Pruning Frameworkcited by 4
  4. 2024Bored to Death: Artificial Intelligence Research Reveals the Role of Boredom in Suicide Behaviorcited by 3
  5. 2021On Semantic Cognition, Inductive Generalization, and Language Modelscited by 2
  6. 2023A Picture May Be Worth a Thousand Lives: An Interpretable Artificial Intelligence Strategy for Predictions of Suicide Risk from Social Media Imagescited by 1
  7. 2023Controllable Multi-document Summarization: Coverage & Coherence Intuitive Policy with Large Language Model Based Rewardscited by 1
  8. 2023Episodic Memory Theory for the Mechanistic Interpretation of Recurrent Neural Networkscited by 0
  9. 2024TinyEmo: Scaling down Emotional Reasoning via Metric Projectioncited by 0
  10. 2026A Mechanistic Understanding of Pronoun Fidelity in LLMscited by 0