Key Points

1. Large Language Models (LLMs) have shown effectiveness in diverse tasks related to table processing and have sparked interest in academia and industry. Tables, essential for everyday tasks, are often structured data that pose unique challenges for LLMs due to their multidimensional nature and complex reasoning requirements.

2. Early research focused on pre-training or fine-tuning neural language models for table tasks like Table QA and fact verification, but newer LLM-based approaches tackle table tasks in three primary ways: instruction-tuning, prompting, and agent-based approaches.

3. The main contribution of the survey is its extensive coverage of a wide range of table tasks, including table manipulation and advanced data analysis.

4. Tables are often structured as spreadsheets, web tables, or databases, each with its own challenges for LLMs due to the differences in data representation and interpretation.

5. The survey categorizes methods based on the latest paradigms in LLM usage, specifically focusing on instruction-tuning, prompting, and LLM-powered agent approaches.

6. New benchmarks and datasets have been proposed to evaluate the robustness, human-involved labeling, and larger scale applications of LLMs in table processing and advanced data analysis tasks.

7. Task-specific fine-tuning methods and instruction-tuning methods are discussed as ways to adapt models for specific table tasks, showcasing the potential of LLMs in handling tables while preserving their text comprehension abilities.

8. Research discusses the challenges and limitations of current methods, like limited transferability, high cost, and privacy issues, in addition to providing potential solutions for private deployment and simplifying the prompting and agent-based methods.

9. Overall, the paper provides a comprehensive overview of the challenges, methods, and potential solutions for using LLMs in table processing tasks and highlights the need for benchmarks that incorporate real-world requirements.

Summary

The survey paper reviewed the use of Large Language Models (LLMs) for table processing tasks, emphasizing traditional and newly emphasized aspects of table tasks and recent paradigms in LLM usage. The paper highlighted challenges in deploying LLMs for table manipulation and advanced data analysis.

Understanding Table Processing and LLMs
The paper covered the essential nature of tables, the challenges LLMs face in understanding the multidimensional aspects of tables, and the complexities of reasoning, data preparation, and utilizing external tools for table processing. It discussed traditional approaches to pre-training and fine-tuning small language models and emphasized recent paradigms such as instruction-tuning, prompting, and agent-based approaches within the realm of LLMs.
The survey overviewed a wide range of table tasks, including table manipulation and advanced data analysis, categorizing methods based on the latest paradigms in LLM usage. It addressed existing benchmarks and proposed new benchmark datasets, emphasizing the robustness, human-involved labeling, and larger scales of evaluations.

Limitations and Challenges
The paper also discussed the limitations and challenges of current LLM-based approaches, including transferability issues, cost concerns, and privacy issues. It suggested potential solutions for private deployment, simplification of prompting and agent-based methods, and the need to incorporate real-world requirements in benchmarks for table processing tasks.

Comparison with Related Surveys
The paper reviewed related surveys and highlighted the novelty and comprehensiveness of the current survey in addressing a wide range of table tasks and recent paradigms in LLM usage, particularly focusing on instruction-tuning, prompting, and LLM-powered agent approaches.

The survey provided a detailed overview of the state-of-the-art in the use of LLMs for table processing, highlighting the challenges, limitations, and potential future directions for research in this domain. The comprehensive coverage of table tasks, LLM paradigms, and challenges makes this survey a valuable resource for researchers and practitioners working in the field of language models and table processing.

Reference: https://arxiv.org/abs/2402.05121