In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive
self-supervised learning to model large-scale heterogeneous data. Many existing foundation …

Automating discovery in mathematics and science will require sophisticated methods of
information extraction and abstract reasoning, including models that can convincingly …

Recent advances in natural language processing, primarily propelled by Large Language
Models (LLMs), have showcased their remarkable capabilities grounded in in-context …

Although most current large multimodal models (LMMs) can already understand photos of
natural scenes and portraits, their understanding of abstract images, eg, charts, maps, or …

The reflection capacity of Large Language Model (LLM) has garnered extensive attention. A
post-hoc prompting strategy, eg, reflexion and self-refine, refines LLM's response based on …

Generating mathematical equations from natural language requires an accurate
understanding of the relations among math expressions. Existing approaches can be …

Automated mathematical problem-solving represents a unique intersection of natural
language processing (NLP) and mathematical reasoning, posing significant challenges in …

Automatic solving math word problems (MWPs) is a number-intensive application in natural
language processing (NLP). However, these existing methods are far from achieving …

Knowledge of the medical decision process, which can be modeled as medical decision
trees (MDTs), is critical to build clinical decision support systems. However, the current MDT …

Solving math word problems (MWP) is a challenging task for natural language processing
systems, as it requires to not only identify and comprehend the problem description within …