## 3 Materials and Methods
### Question Answering Method
[Other Action Definition...]

\begin{table}
\begin{tabular}{l} \hline \hline
**System Prompt** \\ \hline role: You are a table reasoning expert who strictly follows action definitions to process tabular data and selects the next action based on the established operation chain. \\ rule: Directly output the next action and Check the progress status of the current action chain \\ instruction: Using the Action Definition, analyze the table data and select the next action to generate from the Candidate Actions based on the established action chain. \\ \hline
**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. \\ \hline
[Other Action Definition...]

\begin{table}
\begin{tabular}{l} \hline \hline
**System Prompt** \\ \hline \hline role: You are a table reasoning expert who strictly follows action definitions to process tabular data and selects the next action based on the established operation chain. \\ rule: Directly output the next action and Check the progress status of the current action chain \\ instruction: Using the Action Definition, analyze the table data and select the next action to generate from the Candidate Actions based on the established action chain. \\ \hline
**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. \\ \hline
[Other Action Definition...]

## 4 Experiments

### Datasets

As shown in Table 2, the TabFact, FeTaQA, and WikiTQ datasets are selected to train and evaluate the method proposed in this paper. TabFact [36] is a dataset for table verification tasks, containing approximately 118,000 statements generated based on Wikipedia tables. These tables cover multiple subject areas, such as sports, people, movies, etc. Each piece of data includes a table, a statement, and a truth-value annotation (True or False) for the statement. The data of TabFact is divided into a training set and a test set. The training set is used to train the model to identify the authenticity of statements, and the test set is used for model verification. During the data processing, the model not only needs to extract table information but also perform complex reasoning, such as comparing data between multiple cells. TabFact is mainly used to verify the authenticity of table-based factual statements and is suitable for research in the fields of information extraction and table QA.