Large Language Models (LLMs) went from non-existent to ubiquitous in the machine
learning discourse within a few years. Due to the fast pace of the field, it is difficult to identify …

Transformer language models have received widespread public attention, yet their
generated text is often surprising even to NLP researchers. In this survey, we discuss over …

Step-by-step reasoning approaches like chain of thought (CoT) have proved to be very
effective in inducing reasoning capabilities in large language models. However, the success …

Large language models (LLMs) have demonstrated remarkable capabilities across a broad
spectrum of tasks. They have attracted significant attention and been deployed in numerous …

Mathematical reasoning serves as a cornerstone for assessing the fundamental cognitive
capabilities of human intelligence. In recent times, there has been a notable surge in the …

Mathematical reasoning in large language models (LMs) has garnered significant attention
in recent work, but there is a limited understanding of how these models process and store …

Large Language Models (LLMs) are at the forefront of NLP achievements but fall short in
dealing with shortcut learning, factual inconsistency, and vulnerability to adversarial inputs …

ABSTRACT Large Language Models (LLMs), such as ChatGPT, demonstrate a strong
understanding of human natural language and have been explored and applied in various …

The ability to perform causal reasoning is widely considered a core feature of intelligence. In
this work, we investigate whether large language models (LLMs) can coherently reason …

Despite the notable advancements of existing prompting methods, such as In-Context
Learning and Chain-of-Thought for Large Language Models (LLMs), they still face …