Augment before You Try: Knowledge-Enhanced Table Question Answering via Table Expansion (2025)

Abstract: Table question answering is a popular task that assesses a model’s ability to understand and interact with structured data. However, the given table often does not contain sufficient information to answer the question, necessitating the integration of external knowledge. Existing methods either convert both the table and external knowledge into text, which neglects the structured nature of the table; or they embed queries for external sources in the interaction with the table, which complicates the process. In this paper, we propose a simple yet effective method to integrate external information in a given table. Our method first constructs an augmenting table containing the missing information and then generates a SQL query over the two tables to answer the question. Experiments show that our method outperforms strong baselines on three table QA benchmarks.

## introduction tables are ubiquitous types of information sources that have attracted significant attention in the nlp community. researchers have developed models to perform various tabular tasks, including table question answering (qa) @xcite @xcite @xcite , table fact verification @xcite , table-to-text generation @xcite @xcite , etc. a critical challenge in these tasks is that tables often lack sufficient information for the task at hand, which necessitates the integration of additional knowledge. for example, in figure 1 , to answer the question 'how many chords have a root not based on a sharp or flat note?', a model needs to have the knowledge of whether each root is based on a sharp or flat note, which is not provided in the table and can only be obtained from external sources. existing methods for integrating information from tables and external sources can be mainly categorized into two groups. the first method, exemplified by program-of-thought @xcite , linearizes the table into text and combines it with external knowledge in textual format @xcite . however, the linearized table no longer has the structured format, making it difficult to retrieve required values from the table and perform comparisons and calculations. an alternative, binder @xcite , combines the symbolic language execution with large language models (llms). it interacts with the table through symbolic language like sql, which maintains the structured format. part of the sql query is replaced with an llm query that extracts knowledge from the llm for further sql execution. for instance, in figure 1 (b), the method queries llms for whether each root is sharp or flat and uses the results as a filtering criterion in a sql statement. however, it requires the model to learn to embed llm queries in the standard sql language, which differs substantially from the sql statements the model has been trained on. as a result, it is more likely to generate syntactically wrong statements that lead to execution errors. in this paper, we propose a simple yet effective method for combining external knowledge with a given table. as shown in figure 1 (c ), our method starts by analyzing the additional information required for answering the question. it then queries a knowledge source for the information and organizes the results in a tabular format. this newly created table augments the original table with additional information, and a sql query is generated to obtain the answer from the two tables. such an augment-then-generate pipeline eliminates the need to embed llm queries in sql statements while preserving the structured format of the table. we evaluate our method on three table qa datasets that require different types of external knowledge @xcite @xcite . our method outperforms or matches strong baselines on all datasets. particularly, it demonstrates significant improvements over program-of-thought in questions with large tables or require complex tabular operations, and compared to binder, it exhibits fewer execution errors and achieves better performance. table qa task combines structured data reasoning with text understanding. traditional methods parse questions into executable commands to retrieve and process data from the table to obtain answers @xcite @xcite @xcite . however, these methods require question-related information to present the table in a rigorous format, which is limited when applied to web tables that often do not have a clean schema. recent works pre-train neural models on large-scale tabular data, and directly encode tables and generate answers in an end-to-end fashion @xcite @xcite @xcite . to reduce the training cost, some works leverage llms to read and reason over tables @xcite @xcite . although end-to-end methods excel on table qa benchmarks, their predictions lack interpretability and are not robust to input perturbations @xcite . for this reason, recent works combine llms with symbolic language execution. particularly, cheng et al. (2023) incorporates function calls to llms in sql statements. ye et al. (2023) decomposes the question and table into subproblems solvable by sql queries. chen et al. (2023) generates the reasoning process as python programs. a recent work (wang et al., 2024) dynamically updates the table in the reasoning process. they employ llms to iteratively generate operations such as selecting a subset of rows or adding a new column, and the final resulting table is fed to llms to generate the answer. however, their chain of operations is prone to error propagation, while our method retains the original table content and augments it with required information. ## experiments we evaluate our method on table qa benchmarks, focusing on two types of questions that might require external knowledge from different sources. ## conclusion we propose a simple method that augments a table by creating a new table that contains information from external sources. the llm then generates a sql query to answer the question. experiments on three table qa benchmarks show that our method outperforms or matches strong baselines.