## 3 Materials and Methods
### Question Answering Method
FeTaQA [5] is a table QA dataset for few-shot learning, mainly used to answer complex questions through natural language generation. FeTaQA contains approximately 10,000 question-answer pairs generated based on Wikipedia tables. Each piece of data includes a table, a complex query question, and a detailed answer. The question types cover various complex reasoning scenarios such as statistics, comparison, and induction, requiring the model to extract and integrate multiple pieces of relevant information from the table. The FeTaQA dataset supports few-shot and zero-shot learning and is suitable for evaluating the natural language generation ability of models in QA systems and information generation tasks. WikiTableQuestions [35] is an open-domain dataset for table QA, containing approximately 22,000 question-answer pairs generated based on Wikipedia tables. The questions in this dataset usually involve operations such as combination, filtering, and sorting of information within the table. The question types are diverse, such as \"Find the highest value\" and \"List the entries that meet specific conditions\". The dataset is divided into a training set and a test set, which are used to train the model to understand and answer \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Train** & **Test** & **For Fine-Tuning** \\ \hline TabFact & 13,182 & 1695 & 13,182 \\ WikiTQ & 11,321 & 4344 & 11,321 \\ FeTaQA & 7033 & 2000 & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Division of train/test split for TabFact, WikiTQ, and FeTaQA datasetsdata-based questions from tables. The WikiTableQuestions dataset has high requirements for the model's table understanding ability and is an important evaluation dataset for multi-step reasoning and information integration tasks. Among them, TabFact and WikiTQ are in-domain datasets, while FeTaQA is an out-of-domain dataset. When fine-tuning the model, the planning data generated from the training sets of TabFact and WikiTQ are used as the dataset for model fine-tuning in this paper. ### Baseline

To comprehensively evaluate model performance, we categorize baseline methods into two classes: Generic Reasoning and Small Language Model (SLM) Reasoning. **Generic Reasoning**

* **gpt-3.5**[37; 38]: Directly guides GPT-3.5 to generate answers via zero-shot (0-shot) and few-shot (2/4-shot) prompting. * **Text-to-SQL**[23]: Converts natural language questions into SQL queries for table-based reasoning. * **Dater**[19]: Employs sub-table decomposition strategy with LLaMA2-13b for end-to-end table reasoning. * **Chain-of-Thought**[39]: Guides GPT-3.5 through step-by-step reasoning via explicit reasoning chains. **Small Language Model Reasoning. ** This category focuses on the reasoning performance of 7B-8B scale models, comparing pre-finetuning and post-finetuning effectiveness:

* **Zero-Shot Reasoning**[40]: Direct inference using base versions of Qwen2-7b and LLama3-8b. * **Instruction-Finetuned Reasoning**: Uses instruction-fine-tuned variants Qwen2-7b-Instruct and LLama3-8b-Instruct. ### Results

In the experiments, the indicators of directly using ChatGPT for QA in zero-shot and few-shot scenarios are compared, as well as the application of the COT method in table QA and the application of the Text-to-SQL method in table QA. The results are shown in Table 3.

It can be seen that after using the method pipeline proposed in this paper, even on a 7B small-scale model, the performance metrics show significant improvements. The fine-tuned model achieves an increase in accuracy of 4% compared to the non-fine-tuned version on the TabFact and WikiTQ datasets. TQAgent, particularly when integrated with instruction-fine-tuned models, demonstrates enhanced performance on these datasets. This improvement stems from TQAgent's dynamic operation chain planning and multi-step reasoning capabilities, which enable more effective parsing and manipulation of tabular data.