Papers by Marcell Fekete

2 papers
Linguistically Grounded Analysis of Language Models using Shapley Head Values (2025.findings-naacl)

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Challenge: Existing methods for probing language models for morphosyntactic constructions are not well understood . language models gain knowledge of grammatical phenomena during pretraining, but exactly how this knowledge is encoded is not well established.
Approach: They propose a method for probing language models via Shapley Head Values . they use a BLiMP dataset to test their method on linguistic constructions based on a Shaply Head Value method .
Outcome: The proposed method can be used to investigate linguistic knowledge in language models . it shows that attention heads responsible for processing related linguistic phenomena cluster together .
Limited-Resource Adapters Are Regularizers, Not Linguists (2025.acl-short)

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Challenge: Existing studies show that cross-lingual transfer from high-resource languages is promising for low-resourced machine translation.
Approach: They propose to use adapter souping and cross-attention fine-tuning to leverage language transfer for Creoles, an under-served group of low-resource languages.
Outcome: The proposed method improves performance over baselines but not meaningfully with adapters.

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