Generating and Solving Complex Transfer Type Arithmetic Word Problems

Most existing arithmetic word problem (AWP) solvers focus on solving simple examples. Transfer case-AWPs (TC-AWPs) involve scenarios where objects are transferred between agents. The widely used AWP datasets mainly consist of simple TC-AWPs (problems that involve a single object transfer). Current large language models (LLMs) are capable of solving most of these simple TC-AWPs effectively. In this work, we focus on assessing the solving capability of LLMs (ChatGPT and Gemini) for complex TC-AWPs (where multiple types of objects are transferred or more than one transfer of an object is performed). Since the popular AWP datasets contain only simple TC-AWPs, we first generate complex TC-AWPs using an ontological approach. We utilize these complex examples to assess LLMs' word-problem-solving capabilities. We observe that the accuracy of LLMs falls down rapidly as the number of object transfers is increased to 3 or 4. An approach for solving TC-AWPs using ontologies and M/L exists in the literature. We propose an extension of this approach that can handle complex TC-AWPs and find that, compared to the current LLMs, the proposed solution gives better accuracy for complex TC-AWPs. We analyze the failed cases of the LLM approach and find that the reasoning capabilities of LLMs need a lot of improvement.