Solving Arithmetic Word Problems by Synergizing Large Language Model and Scene-Aware Syntax–Semantics Method

Abstract

Developing Arithmetic Word Problem (AWP) -solving algorithms has recently become one of the hottest research areas because it can simultaneously advance general artificial intelligence and the application of AI technology in education. This paper presents a novel algorithm for solving AWPs by synergizing Large Language Models (LLMs) with the Scene-Aware Syntax–Semantics (S2) method. The innovation of this algorithm lies in leveraging the LLM to divide problems into multiple scenes, thereby enhancing the relation-flow approach in the processes of relation extraction and reasoning. Our algorithm consists of three components: scene decomposer, relation extractor, and symbolic solver. In the scene decomposer, we propose the Chain-Of-Scene (COS) method. It dynamically constructs prompts for the LLM using a retrieval-augmented strategy, thus enabling the chain-formed generation of scenes from the input problem. In the relation extractor, we introduce the Scene-Aware S2 method, which utilizes syntax–semantics models to match the text within each scene and convert it into relations. This allows for the efficient and accurate extraction of explicit and implicit relations. Finally, a symbolic solver is employed to reason through the set of relations to derive the solution. Experimental results on six authoritative datasets demonstrate that the proposed algorithm achieves an average solving accuracy of 90.4%, outperforming the State-Of-The-Art (SOTA) algorithm by 1.1%. The case study further illustrates that it outputs more reliable solutions than baseline algorithms. These findings have significant implications for promoting smart education and developing personalized intelligent tutoring systems.