Improving Table-Based Question Answering with New Strategies

Table-based Question Answering (TQA) is a process where questions are answered using information found in tables. Tables are common in many documents, such as financial reports and statistical summaries, and knowing how to read them is important. However, answering questions based on tables can be tough because of the complex way tables are set up and the need for careful Reasoning about the questions asked.

#The Challenge of TQA

One of the main difficulties in TQA arises from the variety of table structures. Some tables have different layers, such as multiple headers or grouped data, which makes it hard for computers, including advanced algorithms, to extract the right information. Additionally, the questions people ask can require deep thinking and a good understanding of the context, meaning the computer must analyze multiple pieces of information at once.

Recently, Large Language Models (LLMs) have shown a strong ability to understand content and perform reasoning. This gives hope for improving TQA processes. However, when dealing with more complicated tables, even advanced LLMs do not always provide the best answers. In many cases, these models struggle because the tasks are too complex to handle without additional help.

#Approaches to Tackle Challenges

To simplify the TQA process, some methods have been developed. These approaches focus on breaking down complex tasks so the LLM can better manage them. This might involve finding which parts of the table are relevant to the question and focusing on those areas. This helps the model work with what it needs without getting lost in unnecessary details.

However, while these strategies can be effective, they often do not take full advantage of the reasoning that occurs during the simplification process. If important pieces of information are left out during this simplification, LLMs may struggle to find the correct answer since they rely on accurate data. This shows the need for a more comprehensive method that combines simplification with reasoning.

#Human-Inspired Reasoning

When looking at how humans tackle complex TQA tasks, it becomes clear that an organized reasoning process is essential. Typically, people follow a two-step method. First, they analyze the question and understand the table's layout to find relevant information. Then, they use this information to come to an answer, step by step. Though these steps seem separate, they are connected in the reasoning process.

Given these observations, researchers aim to improve TQA performance by enhancing how LLMs reason. One suggested method is a two-stage “Seek-and-Solve” process. In the first stage, the model seeks information relevant to the question and generates a logical path that shows its reasoning. In the second stage, this logical path is used to answer the question effectively, making sure the model does not start reasoning from scratch again.

#The Seek-and-Solve Pipeline

The Seek-and-Solve pipeline includes two main parts that work together.

#Stage 1: Seek

In the Seek stage, the LLM is directed to focus on understanding the table and analyzing the question first. Here, the model maps out the table’s structure into a tree format, making it easier to locate relevant details. Each part of the table corresponds to a specific node in this tree, allowing the model to access the necessary information.

Once the structure is set, the model is prompted to identify Tuples, or relevant data points, that will help in answering the question. It separates the selected information into two parts: the reasoning it used to find the information and the actual relevant data it identified.

#Stage 2: Solve

In this Solve stage, the model answers the question using the insights gained from the Seek stage. The logical reasoning developed earlier is used to guide the answering process. By integrating the reasoning from the first stage, this approach helps improve the overall accuracy and coherence of the answer.

Options for this stage can vary. The model may use the entire table or focus on a smaller section derived from the previous step. It may also refer back to the identified tuples or follow a structured path to reach the answer, enhancing the reasoning process further.

#The Compact TQA-Solving Prompt

Another advancement is the creation of a compact single-stage TQA-solving prompt that combines the two stages of the Seek-and-Solve pipeline. This prompt takes the entire table and all the relevant data points as input and uses examples to guide the model's reasoning. The integrated logical reasoning from both stages forms a comprehensive path that mimics the way humans solve these tasks.

This new prompt has shown results that are nearly as effective as the two-stage process but is simpler to use. By providing support through demo examples, it reinforces the model’s ability to tackle complex TQA tasks effectively.

#Experimental Evaluation

To assess these methods, researchers conducted a series of experiments using two different datasets known for their challenging questions and complex table structures: HiTab and WikiTableQuestions. HiTab consists of real-world tables from reports, while WikiTableQuestions includes questions about tables from Wikipedia articles.

In both tests, various prompts were tested to see which combination provided the best results. The findings indicated that when the model reasoned using the logical paths from the Seek stage, it was much more successful compared to when it worked with raw data alone. This highlights the importance of guiding the reasoning process effectively.

#Error Tolerance Analysis

Another aspect studied was how the model handled Errors. It was found that if the model in the first stage made mistakes while seeking information, having a more capable model in the second stage could sometimes correct those errors. However, this correction depended on whether task simplifications were used.

The experiments showed that when simplifying tasks, the second model struggled to fix the errors from the first stage. Yet, when no simplification was applied, the more advanced model performed better and corrected mistakes effectively.

#Conclusions

This research demonstrates a significant improvement in TQA by utilizing the reasoning abilities of LLMs. By introducing a Seek-and-Solve pipeline, the process of reasoning is structured similarly to how humans approach complex tasks. The compact TQA-solving prompt further enhances this by combining both reasoning stages into an easy-to-use format.

Overall, the findings indicate that effectively guiding the reasoning of LLMs can lead to substantial gains in solving complex TQA tasks. Future work will likely focus on refining these methods further and exploring their applications in various fields. This approach could lead to more reliable and accurate systems for handling queries based on tabular data, making it easier for people to access and understand information quickly and effectively.