# Holistic Spatial Reasoning for Chinese Spatial Language Understanding
## 2 Related Work  
Spatial language understanding is a fundamental task in natural language understanding. One of the essential functions of natural language is the expression of spatial relationships between objects. An early method of spatial information extraction was introduced in the form of spatial role labeling (SpRL) by [PERSON] et al. [2]. Furthermore, SemEval2012 introduced the SRL task while mainly focusing on static spatial relations, and SemEval-2013 expanded static spatial relations to capture fine-grained semantics. SemEval-2015 [15] first proposed the evaluation of implementation systems for the SpaceEval annotation scheme, which is the current spatial information annotation scheme, and many spatial information extraction systems have been developed based on it [16; 17; 18]. Multimodal approaches to spatial understanding have also been recently proposed. In CLEF 2017, [PERSON] et al. [19] proposed multimodal spatial role labeling (mSpRL). The authors of [PERSON] et al. [20; 21] proposed the task of visual spatial description, thus extending the line of spatial language understanding. Differing from most efforts that focused on the English context, CCL2021 ([[http://ccl.pku.edu.cn:8084/SpaCE2021/task](http://ccl.pku.edu.cn:8084/SpaCE2021/task)]([http://ccl.pku.edu.cn:8084/SpaCE2021/task](http://ccl.pku.edu.cn:8084/SpaCE2021/task)), accessed on 1 September 2023) was the first proposal of the SpaCE task, which used a non-English language for spatial language understanding. In this study, we follow this work and propose an LLM-based framework for better spatial reasoning. Prior research on spatial language understanding heavily relied on supervised learning and fine-tuned pre-trained models while using specific datasets with extensive labeled data [2; 22; 23], thus achieving remarkable performance. While traditional supervised frameworks face challenges such as the need for extensive labeled data and weak generalizability, recent LLMs have emerged as a solution to these issues. LLMs (e.g., ChatGPT [24], LLaMA [25], and Vacuna [26]) have garnered unprecedented attention and exhibit human  
Figure 2: The overall framework of our chain-of-thought spatial reasoning. level language understanding, thus amassing rich linguistic and commonsense knowledge. LLMs equipped with the prompt learning and ICL paradigm [27; 28; 29; 30; 31; 32] have demonstrated robust performance in scenarios with little or even no supervision, which is a remarkable trait. In the context of LLM-based methods, the initial research involved constructing prompts to inquire targets directly. To address the need for in-depth reasoning, researchers have turned to the recent CoT strategy [33; 34; 35; 36] to enhance LLMs' inference capabilities. CoT empowers LLMs to engage in human-like reasoning processes, thus not only providing answers, but also furnishing the underlying thought process behind those answers. However, conventional CoT simply employs a single prompt template such as \"let us think step by step\" to inspire the LLM [37; 38; 39]. These methods improve reasoning performance on larger LLMs but fail to indicate the explicit reasoning steps for the provided task. Beyond single prompt CoT, [PERSON] et al. [40] encourage internal dialogue by forcing the LLM to generate a sequence of intermediate steps for reasoning problems. [PERSON] et al. [41] go a step further; they (automatically) break a complex problem into simpler sub-problems and then solve them in sequence. Previous multi-step CoT methods primarily focus on arithmetic problems or logical problems, which are relatively easily symbolized and generalized. However, these strategies may struggle to maintain control over intermediate reasoning steps when confronted with diverse tasks, making them less applicable to our spatial understanding problem.