GEESE-Generating and Evaluating Explanations for Semantic Entailment: A CALAMITA Challenge

In the GEESE challenge, we present a pipeline to evaluate generated explanations for the task of Recognizing Textual Entailment (RTE) in Italian. The challenge focuses on evaluating the impact of generated explanations on the predictive performance of language models. Using a dataset enriched with human-written explanations, we employ two large language models (LLMs) to generate and utilize explanations for semantic relationships between sentence pairs. Our methodology assesses the quality of generated explanations by measuring changes in prediction accuracy when explanations are provided. Through reproducible experimentation, we establish benchmarks against various baseline approaches, demonstrating the potential of explanation injection to enhance model interpretability and performance.