Supervised and Unsupervised Probing of Shortcut Learning: Case Study on the Emergence and Evolution of Syntactic Heuristics in BERT

Contemporary language models (LMs) such as BERT (Devlin et al., 2019), T5 (Raffel et al., 2023), GPT-4 (OpenAI, 2023), have exhibited remarkable capabilities, effectively addressing long-standing challenges in the field.However, these models rely on shortcut learning, using a decision rule that relies on superficial cues that are spuriously correlated with the labels (Geirhos et al., 2020).In this research, we focus on the reliance on a specific type of shortcuts, namely syntactic heuristics, in BERT when performing Natural Language Inference (NLI), a representative task in Natural Language Understanding (Jeretic et al., 2020).By making use of two probing methods, one supervised, one unsupervised, we investigate where these shortcuts emerge, how they evolve and how they impact the latent knowledge of the LM.Our findings reveal that syntactic heuristics are absent in pretrained models but emerge and evolve as the model is finetuned with datasets of increasing size.The adoption of these shortcuts varies across different hidden layers, with specific layers closer to the output contributing more to this phenomenon.Despite the model's reliance on shortcuts during inference, it retains information relevant to the task, and our supervised and unsupervised probes process this information differently.