Probing Classifiers › Probing Beyond BERT: Larger and Newer Models

The probing methodology is not specific to BERT. It has been applied to GPT-2, GPT-3, T5, and more recent models, with some consistent findings and some surprises.

Larger models generally encode linguistic properties more robustly. Probing accuracy for syntactic tasks tends to be higher in BERT-large than BERT-base, and higher again in models like GPT-3. This is consistent with the general finding that larger models trained on more data develop richer representations that support a wider range of downstream tasks.

The layer structure of probing profiles shifts in larger models. In BERT-base with 12 layers, syntactic information peaks around layer 6. In larger models with more layers, the peak shifts proportionally: in BERT-large with 24 layers, syntactic peaks appear around layers 10 through 14. This suggests that the fraction of total depth devoted to syntactic processing is roughly constant across scales.

GPT-2 and other causal language models show a different profile from BERT. Because GPT-2 uses causal (unidirectional) attention, each token can only attend to previous tokens. This architectural difference affects what information is accessible in individual token representations. Probing GPT-2's representations for syntactic information shows that syntactic structure is still encoded, but less uniformly than in BERT, because some information about future tokens (which a token's syntactic role may depend on) is unavailable.

Instruction-tuned models show somewhat different probing profiles than the base models they derive from. The fine-tuning process modifies the representations in ways that sometimes increase and sometimes decrease the linear accessibility of specific linguistic properties, depending on whether those properties are relevant to the fine-tuning objective.