A matter of framing: The impact of linguistic formalism on probing results (2020.emnlp-main)
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| Challenge: | Pre-trained contextualized encoders have had a major impact on the field of natural language processing. |
| Approach: | They conduct an in-depth cross-formalism layer probing study in role semantics to investigate the linguistic knowledge implicitly learned by pre-trained contextualized encoders. |
| Outcome: | The proposed model outperforms pre-trained models on a range of downstream tasks. |
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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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What Does Parameter-free Probing Really Uncover? (2024.acl-short)
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| Challenge: | BERTology is a new approach to understanding the inner workings of large pretraining language models. |
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| Challenge: | a suite of probing tasks test contextual embeddings for encoding of information about surrounding words . authors: little is known about what information embeddables encode about the context words encode . a recent study shows that contextual embeds can be powerful for many tasks . |
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Probing Pretrained Language Models for Lexical Semantics (2020.emnlp-main)
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| Challenge: | Existing studies have focused on morphosyntactic, semantic, and world knowledge, but it remains unclear to what extent LMs derive lexical type-level knowledge from words in context. |
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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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| Challenge: | Pre-trained language models can be used to perform a wide array of NLP tasks, but their encoding is still a mystery. |
| Approach: | They propose a usage-based probing setup to find an encoding that the model actually uses, and propose 'a use-based approach' they propose to use a behavioral task to remove the linguistic property, and to identify which encodes are used to transfer information from a noun to its head verb. |
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On the Interplay Between Fine-tuning and Sentence-level Probing for Linguistic Knowledge in Pre-trained Transformers (2020.findings-emnlp)
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| Challenge: | linguistic knowledge encoded in pre-trained contextual embeddings is poorly understood . fine-tuning can be used to investigate the representations of pre-train models . |
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Probing Across Time: What Does RoBERTa Know and When? (2021.findings-emnlp)
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| Challenge: | Current approaches to natural language processing rely on fixed artifacts such as language models . current studies have focused on how these models acquire and demonstrate knowledge . |
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