Generating Prototypes for Contradiction Detection Using Large Language Models and Linguistic Rules

Detecting contradictions in text is one of the hardest tasks The intuition is to use linguistic and factual rules where for a language model to comprehend. This is due to the this is applicable, i.e. for contradictions based on antonymy, complex semantic nature of contradictions, and the variety of negations and numeric mismatches. For more complex relations contexts in which they can occur. For this reason, a multitude such as factive or structural contradictions, we instruct of data sets and models have been developed to solve this a generative model to produce new samples, either based task. Meanwhile, the recent onset of large generative language on given premises or on the type description alone. To our models has given rise to new possibilities for problem solving knowledge, this is the first work implementing such a hybrid as well as data augmentation, which we aim to explore in this data generation method with respect to NLI.