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 …

Recently, graph-based deep learning models have exhibited remarkable performance in
generating solution expressions for the math word problem (MWP). However, most of these …

Math Solving requires both semantic understanding and relation reasoning. Most current
approaches treat it as a translation task from natural language to mathematical symbols …

Solving a math word problem requires selecting quantities in it and performing appropriate
arithmetic operations to obtain the answer. For deep learning-based methods, it is vital to …

The growing interest in leveraging large language models is driven by their exceptional
imitation and reasoning capabilities. In-context learning (ICL), a streamlined method, has …

It has been shown that the generation method works well for modeling the math word
problem. To enhance models' performance on math word problems, some studies employ …