Challenge: Contemporary language models (LMs) rely on shortcut learning, using superficial cues that are spuriously correlated with labels.
Approach: They propose to use syntactic heuristics to learn shortcuts in BERT when performing a task in Natural Language Understanding to investigate where these shortcuts emerge, how they evolve and how they impact the latent knowledge of the LM.
Outcome: The proposed model rely on syntactic heuristics when performing a task in Natural Language Understanding.

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Challenge: Language models (LMs) often rely on spurious correlations rather than causally relevant features to improve accuracy and generalizability.
Approach: They propose a benchmark that categorizes shortcuts into occurrence, style, and concept . they aim to explore the nuanced ways shortcuts influence the performance of LMs .
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Do Syntactic Probes Probe Syntax? Experiments with Jabberwocky Probing (2021.naacl-main)

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Challenge: a study of neural language models shows that syntactic probes do not properly isolate syntax.
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What Does Parameter-free Probing Really Uncover? (2024.acl-short)

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Challenge: Probing large language models (LLMs) has been criticized for using pre-defined label-laden target labels.
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Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning (2023.findings-acl)

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Challenge: Large language models (LLMs) have shown great potential for in-context learning, but their robustness and performance on downstream tasks remains limited.
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Perturbed Masking: Parameter-free Probing for Analyzing and Interpreting BERT (2020.acl-main)

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Challenge: Recent pre-trained language models achieve state-of-the-art performance for downstream NLP tasks.
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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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From BERT‘s Point of View: Revealing the Prevailing Contextual Differences (2022.findings-acl)

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Challenge: BERTology is a new approach to understanding the inner workings of large pretraining language models.
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Measuring and Mitigating Shortcut Reliance in Language Models with Probe-Based Representation Entanglement (2026.acl-srw)

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Challenge: Shortcut learning remains a major obstacle to robust NLP systems.
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Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference (P19-1)

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Challenge: lexical overlap heuristics are effective for frequent example types but break down in more challenging cases.
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Unsupervised Natural Language Parsing (Introductory Tutorial) (2021.eacl-tutorials)

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Challenge: Unsupervised parsing learns a syntactic parser from training sentences without parse tree annotations.
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