mlsanityData to 7 Oct 2026

Does My Representation Capture X? Probe-Ably

arXiv 2104.05807 ↗First posted 12 Apr 2021cs.LG cs.AI cs.CLcited by 8

Not on the map. Why?

Abstract (arXiv)

Probing (or diagnostic classification) has become a popular strategy for investigating whether a given set of intermediate features is present in the representations of neural models. Probing studies may have misleading results, but various recent works have suggested more reliable methodologies that compensate for the possible pitfalls of probing. However, these best practices are numerous and fast-evolving. To simplify the process of running a set of probing experiments in line with suggested methodologies, we introduce Probe-Ably: an extendable probing framework which supports and automates the application of probing methods to the user's inputs.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 14 of its 20 references that are in the map. First posted on arXiv 12 Apr 2021.

Builds on

14 of 20 references.

References · 14

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
  2. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
  3. 2016Understanding intermediate layers using linear classifier probescited by 1,279
  4. 2019BERT Rediscovers the Classical NLP Pipelinecited by 1,046
  5. 2019Designing and Interpreting Probes with Control Taskscited by 608
  6. 2019What do you learn from context? Probing for sentence structure in contextualized word representationscited by 436
  7. 2020Information-Theoretic Probing with Minimum Description Lengthcited by 230
  8. 2019The What-If Tool: Interactive Probing of Machine Learning Modelscited by 207
  9. 2020Information-Theoretic Probing for Linguistic Structurecited by 204
  10. 2020Probing Pretrained Language Models for Lexical Semanticscited by 156
  11. 2020The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Modelscited by 125
  12. 2019AllenNLP Interpret: A Framework for Explaining Predictions of NLP Modelscited by 74
2 more
  1. 2020Pareto Probing: Trading Off Accuracy for Complexitycited by 55
  2. 2018Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysiscited by 17

Built on by

Cited by year

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

8 papers.

  1. 2025Mitigating Content Effects on Reasoning in Language Models through Fine-Grained Activation Steeringcited by 18
  2. 2021Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?cited by 10
  3. 2023Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoderscited by 6
  4. 2022Transformers and the representation of biomedical background knowledgecited by 5
  5. 2021Decomposing Natural Logic Inferences in Neural NLIcited by 4
  6. 2022Do Transformers Encode a Foundational Ontology? Probing Abstract Classes in Natural Languagecited by 3
  7. 2022Montague semantics and modifier consistency measurement in neural language modelscited by 0
  8. 2024Enhancing Robustness in Biomedical NLI Models: A Probing Approach for Clinical Trialscited by 0