Mathematical language processing and problem solving represent a confluence of artificial intelligence, natural language processing and symbolic reasoning, aiming to bridge the gap between human ...

A marriage of formal methods and LLMs seeks to harness the strengths of both.

Mathematicians excel at handling complexity and uncertainty. Mathematical reasoning strategies aren't just useful for dilemmas involving numbers. We can apply math mindsets to improve our approach to ...

This study introduces MathEval, a comprehensive benchmarking framework designed to systematically evaluate the mathematical reasoning capabilities of large language models (LLMs). Addressing key ...

A member of our research community, Terhi Vessonen, along with her co-authors (Heidi Hellstrand, Matti Kurkela, Pirjo Aunio, and Anu Laine), has conducted a systematic review and meta-analysis ...

A team of Apple researchers has released a paper scrutinising the mathematical reasoning capabilities of large language models (LLMs), suggesting that while these models can exhibit abstract reasoning ...

Cognitive overload can create a bottleneck during math lessons, but there are simple strategies to clear up students’ brain space for complex problem-solving.