Conditional probing: measuring usable information beyond a baseline (2021.emnlp-main)
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| Challenge: | Existing methods for probing representations are limited to predicting part-of-speech . current methods cannot detect when a representation is predictive of just aspects of part- of-seech not explainable by the word identity. |
| Approach: | They propose to condition on the information in a baseline representation to test whether it is predictive of part-of-speech. |
| Outcome: | The proposed method is based on a theory of usable information called V-information and conditions on the information in the baseline. |
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| Challenge: | Neural models have established state-of-the-art performance on several NLP benchmarks, but little is understood about the mechanisms by which they operate. |
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| Challenge: | 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. |
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| Challenge: | Amnesic probing is a method that focuses on how information is being used, rather than on what information is encoded. |
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Classifier Probes May Just Learn from Linear Context Features (2020.coling-main)
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| Challenge: | Current probing methods can help to better estimate the complexity of learning, but not build a foundation for speculations about the nature of the linguistic structure encoded in the learned representations. |
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Information-Theoretic Probing with Minimum Description Length (2020.emnlp-main)
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| Challenge: | Despite widespread adoption of probes, differences in their accuracy fail to adequately reflect differences in representations. |
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Information-Theoretic Probing for Linguistic Structure (2020.acl-main)
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| Challenge: | Neural networks are the backbone of modern stateof-the-art natural language processing systems. |
| Approach: | They propose an information-theoretic operationalization of probing as estimating mutual information that contradicts received wisdom . they evaluate on a set of ten typologically diverse languages often underrepresented in NLP research—plus English—totalling eleven languages. |
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Probing for the Usage of Grammatical Number (2022.acl-long)
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| Challenge: | Pre-trained language models can be used to perform a wide array of NLP tasks, but their encoding is still a mystery. |
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The Functional Relevance of Probed Information: A Case Study (2023.eacl-main)
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What if This Modified That? Syntactic Interventions with Counterfactual Embeddings (2021.findings-acl)
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| Challenge: | Prior art aims to uncover meaningful properties within model representations, but it is unclear how faithfully such probes portray information that the models actually use. |
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Probing Contextual Language Models for Common Ground with Visual Representations (2021.naacl-main)
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| Challenge: | Contextual language models have attracted great interest in probing what is encoded in their representations. |
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