Papers with masked models

3 papers
Garden Path Recovery in Causal and Masked Language Models (2026.acl-srw)

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Challenge: a linguistics study of garden-path sentences shows that recovery dynamics are important for linguistic evaluation . causal models show larger within-model disambiguation effects than masked models overall .
Approach: They propose to compare garden-path recovery in causal and masked language models . they use 100 English garden- path/control pairs spanning three constructions .
Outcome: The proposed model shows that decoder-only models exhibit sharper disruption at the point of syntactic revision, while encoders appear comparatively buffered at the disambiguator due to right-context access.
Few-shot clinical entity recognition in English, French and Spanish: masked language models outperform generative model prompting (2024.findings-emnlp)

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Challenge: Named Entity Recognition (NER) is a critical task in information extraction that is not covered in recent benchmarks.
Approach: They compare 13 auto-regressive models using prompting and 16 masked models using fine-tuning on 14 NER datasets covering English, French and Spanish.
Outcome: The proposed models outperform auto-regressive models in English, French and Spanish on 14 NER datasets.
Using Shapley interactions to understand how models use structure (2025.acl-long)

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Challenge: Language models are intricately structured systems, and attribution measures are important for understanding how they combine features to influence outputs.
Approach: They use Shapley Taylor interaction indices to examine how language and speech models internally relate and structure their inputs.
Outcome: The proposed methods show that language models encode phonetic interactions . they show that the inputs are more entangled for pairs where a consonant influences a vowel or approximant .

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