## 3 Materials and Methods
### Question Answering Method
This not only deepens the model's comprehension of the table content but also effectively mitigates the \"hallucination\" phenomenon during the reasoning process. In practical operation, when the Planner generates an operation chain, it determines whether to obtain information from the knowledge graph based on the current task requirements and the state of the table. If necessary, it dispatches instructions to the Retriever. The Retriever screens out the content most relevant to the current task, leveraging the information retrieved from the knowledge graph, and supplies it to the Worker for subsequent operations. For example, in a table QA task involving data analysis, the Worker might need to draw on industry knowledge from the knowledge graph to interpret and process the table data more precisely, thereby generating a more rational answer. Furthermore, the knowledge graph can assist the model in handling ambiguous or implicit information. When there is ambiguity in the table data, the context information within the knowledge graph can aid the model in resolving the ambiguity and making moreaccurate judgments. By continuously interacting with the knowledge graph, the model's reasoning ability in dealing with table QA tasks has been remarkably enhanced. It can better address various complex questions and diverse table data, offering users more dependable answers. ### Knowledge-Augmented Fine-Tuning

First, gpt-3.5-turbo is used to generate dynamic reasoning chains, including table operations in intermediate steps. The multi-step operation chains generated during the QA process are used as the basis for fine-tuning data. These data provide the complete process of the model generating operation chains, covering specific steps such as initial parsing, table operations, and information supplementation. Then, based on 7B models like Llama3 and Qwen2, the LoRA technique is employed. The selected high-quality operation chain dataset is used for fine-tuning, focusing on improving the model's performance in generating and executing multi-step reasoning chains. Finally, the fine-tuned model replaces the basic large language model used by the Planner in the QA process. The performance of the fine-tuned model is tested on the benchmark dataset to evaluate its multi-step reasoning accuracy and information supplementation ability in the table QA task. The fine-tuning data are derived from the training datasets of TabFact and WikiTQ, aiming to fine-tune the action planning ability of the large language model. The fine-tuning data consist of 4031 selected action reasoning data instructions. We used Llama-Factory ([[https://github.com/hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)]([https://github.com/hiyouga/LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)), accessed on 7 March 2025) to perform supervised fine-tuning (SFT) on the local A40 GPU. The instruction format is shown in Table 1.

**Action Definition:**

If the table only needs a few rows to tell whether the statement is True or False, we use select_row() to select these rows for it. For example:

table caption : jeep grand cherokee. col : years | displacement | engine | row 1 : 1999-2004 | 4.01(242 cid) | power tech i6 | row 2 : 1999-2004 | 4.71(287 cid) | powertech v8 | row 3 : 2002-2004 | 4.71(287 cid) | high output powertech v8| row 4 : 1999-2001 | 3.11 diesel | 531 ohv diesel i5| row 5 : 2002-2004 | 2.71 diesel | om647 diesel i5| Question: The Jeep Grand Cherokee with the OM647 Diesel I5 had the third lowest numbered displacement. Function: select_row(1, 4, 5) Explanation: The statement wants to check if the OM647 Diesel I5 had the third lowest numbered displacement. We need to know the first three low-numbered displacements and all rows where the engine is OM647 Diesel I5. We select rows 1, 4, and 5.