Exploring the Vertical-Domain Reasoning Capabilities of Large Language Models

• URL: http://arxiv.org/abs/2512.22443v1
• Date: Sat, 27 Dec 2025 02:39:34 GMT
• Title: Exploring the Vertical-Domain Reasoning Capabilities of Large Language Models
• Abstract summary: This study introduces the concept of vertical-domain accounting reasoning and establishes evaluation criteria.<n>We evaluate several representative models, including GLM-6B, GLM-130B, GLM-4, and OpenAI GPT-4, on a set of accounting reasoning tasks.
• Score: 19.821219678322517
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Large Language Models (LLMs) are reshaping learning paradigms, cognitive processes, and research methodologies across a wide range of domains. Integrating LLMs with professional fields and redefining the relationship between LLMs and domain-specific applications has become a critical challenge for promoting enterprise digital transformation and broader social development. To effectively integrate LLMs into the accounting domain, it is essential to understand their domain-specific reasoning capabilities. This study introduces the concept of vertical-domain accounting reasoning and establishes evaluation criteria by analyzing the training data characteristics of representative GLM-series models. These criteria provide a foundation for subsequent research on reasoning paradigms and offer benchmarks for improving accounting reasoning performance. Based on this framework, we evaluate several representative models, including GLM-6B, GLM-130B, GLM-4, and OpenAI GPT-4, on a set of accounting reasoning tasks. Experimental results show that different prompt engineering strategies lead to varying degrees of performance improvement across models, with GPT-4 achieving the strongest accounting reasoning capability. However, current LLMs still fall short of real-world application requirements. In particular, further optimization is needed for deployment in enterprise-level accounting scenarios to fully realize the potential value of LLMs in this domain.

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