# Does My Representation Capture X? Probe-Ably

arXiv: https://arxiv.org/abs/2104.05807 · HTML: https://mlsanity.com/paper/2104.05807/

## Key facts

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

## Builds on (most cited first, 14 of 14)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Understanding intermediate layers using linear classifier probes](https://mlsanity.com/paper/1610.01644/) (2016)
- [BERT Rediscovers the Classical NLP Pipeline](https://mlsanity.com/paper/1905.05950/) (2019)
- [Designing and Interpreting Probes with Control Tasks](https://mlsanity.com/paper/1909.03368/) (2019)
- [What do you learn from context? Probing for sentence structure in contextualized word representations](https://mlsanity.com/paper/1905.06316/) (2019)
- [Information-Theoretic Probing with Minimum Description Length](https://mlsanity.com/paper/2003.12298/) (2020)
- [The What-If Tool: Interactive Probing of Machine Learning Models](https://mlsanity.com/paper/1907.04135/) (2019)
- [Information-Theoretic Probing for Linguistic Structure](https://mlsanity.com/paper/2004.03061/) (2020)
- [Probing Pretrained Language Models for Lexical Semantics](https://mlsanity.com/paper/2010.05731/) (2020)
- [The Language Interpretability Tool: Extensible, Interactive Visualizations and Analysis for NLP Models](https://mlsanity.com/paper/2008.05122/) (2020)
- [AllenNLP Interpret: A Framework for Explaining Predictions of NLP Models](https://mlsanity.com/paper/1909.09251/) (2019)
- [Pareto Probing: Trading Off Accuracy for Complexity](https://mlsanity.com/paper/2010.02180/) (2020)
- [Language Modeling Teaches You More Syntax than Translation Does: Lessons Learned Through Auxiliary Task Analysis](https://mlsanity.com/paper/1809.10040/) (2018)

## Built on by (most cited first, 8 of 8)

- [Mitigating Content Effects on Reasoning in Language Models through Fine-Grained Activation Steering](https://mlsanity.com/paper/2505.12189/) (2025)
- [Grounding Natural Language Instructions: Can Large Language Models Capture Spatial Information?](https://mlsanity.com/paper/2109.08634/) (2021)
- [Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoders](https://mlsanity.com/paper/2311.08579/) (2023)
- [Transformers and the representation of biomedical background knowledge](https://mlsanity.com/paper/2202.02432/) (2022)
- [Decomposing Natural Logic Inferences in Neural NLI](https://mlsanity.com/paper/2112.08289/) (2021)
- [Do Transformers Encode a Foundational Ontology? Probing Abstract Classes in Natural Language](https://mlsanity.com/paper/2201.10262/) (2022)
- [Montague semantics and modifier consistency measurement in neural language models](https://mlsanity.com/paper/2212.04310/) (2022)
- [Enhancing Robustness in Biomedical NLI Models: A Probing Approach for Clinical Trials](https://mlsanity.com/paper/2402.02558/) (2024)

## 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.