Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs
This work provides insights into the internal mechanisms of language models for researchers in NLP and cognitive science, but it is incremental as it builds on existing probing and minimal pair benchmarks.
The authors tackled the problem of understanding how neural language models capture linguistic structures by introducing a decoding probing method using minimal pairs, revealing that self-supervised models capture abstract linguistic features in intermediate layers, with GPT-2 requiring more layers for complex sentences.
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.