Decoding Probing: Revealing Internal Linguistic Structures in Neural Language Models using Minimal Pairs (2403.17299v1)

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 LLMs layer by layer. By treating the LLM as thebrain' and its representations as `neural activations', we decode grammaticality labels of minimal pairs from the intermediate layers' representations. This approach reveals: 1) Self-supervised LLMs capture abstract linguistic structures in intermediate layers that GloVe and RNN LLMs 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.