## 2 Related Works  
**TableQA:** Recent advancements in TableQA focus on enhancing reasoning accuracy and interpretability through modular frameworks and specialized model tuning. The emergence and development of LLM [15; 16] have also spurred a wave of advancements in the TableQA field [17]. TaPERA [18] proposed a three-step pipeline integrating content planning, execution-based reasoning, and answer generation to mitigate hallucination issues in long-form answers; [PERSON] et al. [19] leveraged LLMs as decomposers to break down complex questions and tables into tractable sub-components, achieving human-level performance on TabFact; [PERSON] et al. [7] introduced OpticalTable-SQA, fine-tuning table-based models for optical-materials data; [PERSON] et al. [20] developed the Text2 Analysis benchmark, extending TableQA to advanced operations like forecasting.  
Recently, some studies have shifted attention toward table-tuning and systematic evaluation. Table-GPT [21] enhances generalizability through table-tuning of LLMs; [PERSON] et al. [22] systematically evaluated LLMs' structural understanding and proposed prompting strategies for improved table reasoning. These works collectively advance TableQA through decomposition strategies, domain specialization, and systematic evaluation frameworks. [PERSON] et al. [23] and [PERSON] et al. [24] aimed to utilize NL2 SQL techniques to convert tableQA tasks into SQL queries. TableLlama [25] and SheetAgent [26] leveraged fine-tuning datasets to unlock the potential of small-parameter large language models in the field of table reasoning.  
**LLM Hallucination** Large language model hallucinations refer to instances where models generate plausible but factually incorrect or nonsensical information. The phenomenon of hallucinations in LLMs has garnered significant attention due to its implications for reliability and trustworthiness. Prior studies have explored diverse methodologies to detect, mitigate, and understand this issue. [PERSON] et al. [27] highlighted the integration of Knowledge Graphs (KGs) to ground LLM outputs in structured factual data, a strategy further advanced by [PERSON] et al. [28], who proposed autonomous KG-based retrofitting to refine LLM responses during reasoning. For hallucination detection, [PERSON] [29] introduced Monte Carlo simulations on token probabilities to identify low-confidence outputs, while [PERSON] et al. [30] leveraged unlabeled LLM generations through the HaloScope framework to train robust classifiers. Architectural innovations, such as combining Graph Neural Networks (GNNs) with LLMs [31], demonstrate enhanced factual consistency by processing relational and textual data jointly. Paradoxically, [PERSON] et al. [32] revealed that controlled hallucination via null-shot prompting can improve performance in reasoning tasks, challenging conventional mitigation paradigms. Empirical investigations by [PERSON] [33] further uncover domain-specific impacts of hallucinations, such as degraded motor skill learning in physical education. Underlying these challenges, [PERSON] [34] attribute hallucinations to training data limitations, particularly on controversial or niche topics. Collectively, the issue of hallucinations remains a critical challenge in the field of LLM. This paper addresses the problem by introducing Knowledge Graphs as external world knowledge to mitigate hallucinations in large models.