@inproceedings{he-etal-2024-decoding-probing,
title = "Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs",
author = "He, Linyang and
Chen, Peili and
Nie, Ercong and
Li, Yuanning and
Brennan, Jonathan R.",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.402",
pages = "4488--4497",
abstract = "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 activations{''}, we decode grammaticality labels of minimal pairs from the intermediate layers{'} representations. This approach reveals: 1) Self-supervised language models capture abstract linguistic structures in intermediate layers that GloVe and RNN language models cannot learn. 2) Information about syntactic grammaticality is robustly captured through the first third layers of GPT-2 and also distributed in later layers. As sentence complexity increases, more layers are required for learning grammatical capabilities. 3) Morphological and semantics/syntax interface-related features are harder to capture than syntax. 4) For Transformer-based models, both embeddings and attentions capture grammatical features but show distinct patterns. Different attention heads exhibit similar tendencies toward various linguistic phenomena, but with varied contributions.",
}
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<abstract>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 activations”, we decode grammaticality labels of minimal pairs from the intermediate layers’ representations. This approach reveals: 1) Self-supervised language models capture abstract linguistic structures in intermediate layers that GloVe and RNN language models cannot learn. 2) Information about syntactic grammaticality is robustly captured through the first third layers of GPT-2 and also distributed in later layers. As sentence complexity increases, more layers are required for learning grammatical capabilities. 3) Morphological and semantics/syntax interface-related features are harder to capture than syntax. 4) For Transformer-based models, both embeddings and attentions capture grammatical features but show distinct patterns. Different attention heads exhibit similar tendencies toward various linguistic phenomena, but with varied contributions.</abstract>
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%0 Conference Proceedings
%T Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs
%A He, Linyang
%A Chen, Peili
%A Nie, Ercong
%A Li, Yuanning
%A Brennan, Jonathan R.
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F he-etal-2024-decoding-probing
%X 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 activations”, we decode grammaticality labels of minimal pairs from the intermediate layers’ representations. This approach reveals: 1) Self-supervised language models capture abstract linguistic structures in intermediate layers that GloVe and RNN language models cannot learn. 2) Information about syntactic grammaticality is robustly captured through the first third layers of GPT-2 and also distributed in later layers. As sentence complexity increases, more layers are required for learning grammatical capabilities. 3) Morphological and semantics/syntax interface-related features are harder to capture than syntax. 4) For Transformer-based models, both embeddings and attentions capture grammatical features but show distinct patterns. Different attention heads exhibit similar tendencies toward various linguistic phenomena, but with varied contributions.
%U https://aclanthology.org/2024.lrec-main.402
%P 4488-4497
Markdown (Informal)
[Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models Using Minimal Pairs](https://aclanthology.org/2024.lrec-main.402) (He et al., LREC-COLING 2024)
ACL