Peili Chen — accepted AI-conference papers

2024

Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs

Linyang He, Peili Chen, Ercong Nie, Yuanning Li, Jonathan R. Brennan

COLING 2024main

Inspired by cognitive neuroscience studies, we introduce a novel “decoding probing” method that uses minimal pairs benchmark (BLiMP) to probe internal linguistic characteristics in neural language models layer by layer. By treating the language model as the brain and its representations as “neural a…

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2024

Do Self-Supervised Speech and Language Models Extract Similar Representations as Human Brain?

Peili Chen, Linyang He, Li Fu, Lu Fan, Edward F. Chang, Yuanning Li

ICASSP 2024accepted

Speech and language models trained through self-supervised learning (SSL) demonstrate strong alignment with brain activity during speech and language perception. However, given their distinct training modalities, it remains unclear whether they correlate with the same neural aspects. We directly add…

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