Uncovering More Shallow Heuristics: Probing the Natural Language Inference Capacities of Transformer-Based Pre-Trained Language Models Using Syllogistic Patterns
This work addresses the problem of understanding model limitations in NLI for researchers, revealing incremental insights into spurious heuristics.
The study investigated the shallow heuristics used by transformer-based pre-trained language models in natural language inference by evaluating them on a custom syllogistic dataset, finding that models rely heavily on symmetries and asymmetries between premise and hypothesis, indicating a lack of generalization.
In this article, we explore the shallow heuristics used by transformer-based pre-trained language models (PLMs) that are fine-tuned for natural language inference (NLI). To do so, we construct or own dataset based on syllogistic, and we evaluate a number of models' performance on our dataset. We find evidence that the models rely heavily on certain shallow heuristics, picking up on symmetries and asymmetries between premise and hypothesis. We suggest that the lack of generalization observable in our study, which is becoming a topic of lively debate in the field, means that the PLMs are currently not learning NLI, but rather spurious heuristics.