Advancing Mathematical Reasoning with Language Models

Mathematical reasoning is a pivotal component of human intelligence, crucial for advancing education and science. This talk delves into the development of language model systems capable of robust mathematical reasoning, marking a significant step toward realizing general artificial intelligence. We introduce multi-modal and knowledge-intensive benchmarks to assess the reasoning capabilities of large language models (LLMs) and vision-language models (VLMs) across real-world contexts, including visual information, tabular data, and scientific domains. This talk advances the field by presenting innovative retrieval and tool-augmented algorithms that enhance LLM capabilities. It concludes by analyzing the latest advances in mathematical reasoning within visual contexts, and highlighting the current challenges and future prospects.