Table Augmentor
The table augmentor aims to convey extra information hidden inherently in the table, beyond the raw data itself, to the LLMs. The augmentation process occurs prior to the official reasoning process, as illustrated in Figure 2 .

It’s worth noting that we can link this process to real large database systems or table applications Xue et al. ( 2023 ) . In standard database systems, extensive work on data cleaning and normalization must be undertaken. During the process, hierarchical augmentation information will be stored and synchronized, including information about the database, tables, and statistical data inside the table. We focus on the latter two levels, as our emphasis lies on the Table QA scenario. In fact, in real-world databases, column names are often stored without semantic meaning and represented by uppercase abbreviations. The data stored may be formatted in equations and abstract symbols, posing challenges in generating SQL queries accurately. Therefore, schema and column feature information need to be predefined and stored in a standardized manner. In this case, we can simplify the process of the table augmentor by migrating augmented information.

In this paper, we leverage LLMs’ inherent knowledge extraction capabilities to augment table information based on the filtered sub-table. Specifically, we design three different augmentation types to suit the needs of downstream stages: schema information, semantic information, and literal representation. The prompt for each category of augmentation is detailed in Appendix F.

Schema information primarily represents the data types of features stored in tables, which facilitates inferring and unifying data formats when reasoning over tables. We extracted three commonly used types in daily analysis: Numerical, Char, and Date types. These types are utilized to standardize and transform table data. Special symbols are preprocessed for Numerical and Char data, and different date representations are uniformly formatted for the Date type. The features ultimately stored in the database for SQL manipulation are transformed into corresponding data types.

Semantic information primarily includes the global semantic information of the table and the semantics about column features. The global table information provide clues for identifying the relevant domain of the table. Utilizing featurespecific semantic information, LLMs can more accurately locate features related to the query. Specifically, when columns are named using acronyms or aliases, the imparted semantics can be pivotal for analysis. The semantic information is transmitted for column filtering in stage 1 and augmented SQL generation in stage 2.

SQL queries often fail to accurately parse the correct format stored in the table. Chain-ofTable (Wang et al., 2024b) improves this by using multiple chain calls. However, by explicitly informing LLMs about the raw data representation format within the table through literal information, the model performs better in the generation of correctly formatted SQL queries in a single LLM call. Unlike semantic information, literal information focuses on the representation format of the data (e.g., extra parentheses, calculation formulas, special expressions), which is efficient for SQL generation.