Recently, tool learning with large language models (LLMs) has emerged as a promising
paradigm for augmenting the capabilities of LLMs to tackle highly complex problems …

Effective training of language models (LMs) for mathematical reasoning tasks demands high-
quality supervised fine-tuning data. Besides obtaining annotations from human experts, a …

Though quite challenging, training a deep neural network for automatically solving Math
Word Problems (MWPs) has increasingly attracted attention due to its significance in …

Effective pre-training of large language models (LLMs) has been challenging due to the
immense resource demands and the complexity of the technical processes involved. This …

Large language models (LLMs) are known to struggle with complicated reasoning tasks
such as math word problems (MWPs). In this paper, we present how analogy from similarly …

Quantitative and numerical comprehension in language is an important task in many fields
like education and finance, but still remains a challenging task for language models. While …

In recent years, language models have emerged into a technology adopted in a wide variety
of applications, nowadays largely exceeding traditional natural language processing tasks …