Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals
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Abstract (arXiv)
A growing body of work makes use of probing to investigate the working of neural models, often considered black boxes. Recently, an ongoing debate emerged surrounding the limitations of the probing paradigm. In this work, we point out the inability to infer behavioral conclusions from probing results and offer an alternative method that focuses on how the information is being used, rather than on what information is encoded. Our method, Amnesic Probing, follows the intuition that the utility of a property for a given task can be assessed by measuring the influence of a causal intervention that removes it from the representation. Equipped with this new analysis tool, we can ask questions that were not possible before, e.g. is part-of-speech information important for word prediction? We perform a series of analyses on BERT to answer these types of questions. Our findings demonstrate that conventional probing performance is not correlated to task importance, and we call for increased scrutiny of claims that draw behavioral or causal conclusions from probing results.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Jun 2020.
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Citing papers per year: 2021: 7; 2022: 3; 2023: 1; 2024: 1.
12 papers.
- 2021The Low-Dimensional Linear Geometry of Contextualized Word Representationscited by 39
- 2022Unit Testing for Concepts in Neural Networkscited by 23
- 2021How much pretraining data do language models need to learn syntax?cited by 21
- 2024Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgettingcited by 15
- 2023Operationalising Representation in Natural Language Processingcited by 14
- 2021The Rediscovery Hypothesis: Language Models Need to Meet Linguisticscited by 7
- 2021Assessing the Syntactic Capabilities of Transformer-based Multilingual Language Modelscited by 7
- 2021On the Evolution of Syntactic Information Encoded by BERT's Contextualized Representationscited by 6
- 2021Morph Call: Probing Morphosyntactic Content of Multilingual Transformerscited by 5
- 2022Probing with Noise: Unpicking the Warp and Weft of Embeddingscited by 3
- 2021Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?cited by 1
- 2022Visualizing the Relationship Between Encoded Linguistic Information and Task Performancecited by 1
- 2024Dual Process Learning: Controlling Use of In-Context vs. In-Weights Strategies with Weight Forgetting28 May 2024
- 2023Operationalising Representation in Natural Language Processing14 Jun 2023
- 2022Probing with Noise: Unpicking the Warp and Weft of Embeddings21 Oct 2022
- 2022Unit Testing for Concepts in Neural Networks28 Jul 2022
- 2022Visualizing the Relationship Between Encoded Linguistic Information and Task Performance29 Mar 2022
- 2021How much pretraining data do language models need to learn syntax?7 Sep 2021
- 2021Do Encoder Representations of Generative Dialogue Models Encode Sufficient Information about the Task ?20 Jun 2021
- 2021The Low-Dimensional Linear Geometry of Contextualized Word Representations15 May 2021
- 2021Assessing the Syntactic Capabilities of Transformer-based Multilingual Language Models10 May 2021
- 2021Morph Call: Probing Morphosyntactic Content of Multilingual Transformers26 Apr 2021
- 2021The Rediscovery Hypothesis: Language Models Need to Meet Linguistics2 Mar 2021
- 2021On the Evolution of Syntactic Information Encoded by BERT's Contextualized Representations27 Jan 2021