Recent years have seen impressive performance of transformer-based models on different
natural language processing tasks. However, it is not clear to what degree the transformers …

Language models (LMs) are becoming the foundation for almost all major language
technologies, but their capabilities, limitations, and risks are not well understood. We present …

Abstract Generative Artificial Intelligence is set to revolutionize healthcare delivery by
transforming traditional patient care into a more personalized, efficient, and proactive …

Automated reasoning with unstructured natural text is a key requirement for many potential
applications of NLP and for developing robust AI systems. Recently, Language Models …

Abstract Machine learning (ML) systems in natural language processing (NLP) face
significant challenges in generalizing to out-of-distribution (OOD) data, where the test …

Complex reasoning aims to draw a correct inference based on complex rules. As a hallmark
of human intelligence, it involves a degree of explicit reading comprehension, interpretation …

Large language models have shown impressive results for multi-hop mathematical
reasoning when the input question is only textual. Many mathematical reasoning problems …

We present LogiGAN, an unsupervised adversarial pre-training framework for improving
logical reasoning abilities of language models. Upon automatic identification of logical …

This study investigates self-assessment tendencies in Large Language Models (LLMs),
examining if patterns resemble human cognitive biases like the Dunning–Kruger effect …

Logical reasoning on natural language is one of the most challenging tasks for deep
learning models. There has been an increasing interest in developing new benchmarks to …