RICA: Evaluating Robust Inference Capabilities Based on Commonsense Axioms

Pre-trained language models (PTLM) have impressive performance on commonsense inference benchmarks, but their ability to practically employ commonsense to communicate with humans is fiercely debated. Prior evaluations of PTLMs have focused on factual world knowledge or the ability to reason when the necessary knowledge is provided explicitly. However, effective communication with humans requires inferences based on implicit commonsense relationships, and robustness despite paraphrasing. In the pursuit of advancing fluid human-AI communication, we propose a new challenge, RICA, that evaluates the capabilities of making commonsense inferences and the robustness of these inferences to language variations. In our work, we develop a systematic procedure to probe PTLMs across three different evaluation settings. Extensive experiments on our generated probe sets show that PTLMs perform no better than random guessing (even with fine-tuning), are heavily impacted by statistical biases, and are not robust to perturbation attacks. Our framework and probe sets can help future work improve PTLMs' inference abilities and robustness to linguistic variations--bringing us closer to more fluid communication.