Large Language Models(LLMs) on Tabular Data: Prediction, Generation, and Understanding -- A Survey (2402.17944v4)

Abstract: Recent breakthroughs in large language modeling have facilitated rigorous exploration of their application in diverse tasks related to tabular data modeling, such as prediction, tabular data synthesis, question answering, and table understanding. Each task presents unique challenges and opportunities. However, there is currently a lack of comprehensive review that summarizes and compares the key techniques, metrics, datasets, models, and optimization approaches in this research domain. This survey aims to address this gap by consolidating recent progress in these areas, offering a thorough survey and taxonomy of the datasets, metrics, and methodologies utilized. It identifies strengths, limitations, unexplored territories, and gaps in the existing literature, while providing some insights for future research directions in this vital and rapidly evolving field. It also provides relevant code and datasets references. Through this comprehensive review, we hope to provide interested readers with pertinent references and insightful perspectives, empowering them with the necessary tools and knowledge to effectively navigate and address the prevailing challenges in the field.

• The paper presents an expansive survey evaluating LLM techniques for tabular data prediction, synthesis, question answering, and table understanding.
• It outlines key preprocessing methods such as serialization, prompt engineering, and table manipulation to adapt tabular data for LLM use.
• The survey highlights challenges including bias, numerical representation issues, and the need for standard benchmarks while suggesting future research directions.

Leveraging LLMs for Tabular Data: A Comprehensive Survey

Overview

Recent advancements in LLMs have opened up a promising avenue for modeling tabular data across various applications, despite being inherently designed for natural language processing tasks. This survey paper offers a detailed examination of how LLMs are applied to tabular data modeling, focusing on prediction tasks, data synthesis, question answering (QA), and table understanding. By identifying the techniques for preparing tabular data for LLMs, comparing methodologies, and highlighting the challenges and future research directions, this survey serves as a foundational piece for researchers and practitioners in the field of Machine Learning (ML) and AI.

Key Techniques for LLM Applications on Tabular Data

The survey outlines critical steps necessary for processing tabular data to make it compatible with LLMs, including:

• Serialization: Transforming tabular data into a text or embedding format, thereby enabling input to LLMs.
• Table Manipulation: Techniques to compact tables for efficient processing, including strategies for handling large datasets that exceed LLM context length limitations.
• Prompt Engineering: Refining input prompts to guide LLMs towards generating accurate outputs by introducing examples, task descriptions, and iterative approaches for complex reasoning.

Methodologies Across Different Tasks

For each of the focal areas—prediction, data synthesis, QA, and table understanding—the paper conducts an extensive review of methodologies, highlighting the typical pipeline, evaluation metrics, and notable models employed. It becomes evident that while LLMs have shown the capability to understand and generate tabular data, the use of prompt engineering, in-context learning examples, and fine-tuning strategies are crucial for enhancing performance on specific tasks.

Challenges and Limitations

Despite the progress, several limitations are emphasized:

• Bias and Fairness: The inherited social bias from training data of LLMs poses a challenge for fairness in applications.
• Hallucination: LLMs' tendency to generate information not present in the input data leads to reliability concerns.
• Numerical and Categorical Representation Issues: Effective encoding of numerical and categorical features remains a technical hurdle.
• Lack of Standard Benchmarks: The field suffers from heterogeneity in datasets and metrics, calling for standardized benchmarks for fair comparison.
• Interpretability and Accessibility: The black-box nature of LLMs complicates result interpretation, and the complexity of model fine-tuning limits accessibility for non-experts.
• Fine-tuning Strategy and Model Grafting: Designing effective fine-tuning strategies and integrating model grafting techniques to include non-text data represent future research directions.

Recommendations and Speculations on Future Developments

The paper advocates for the exploration of advanced tokenization and embedding techniques to better represent tabular data, efforts towards developing unified benchmarks, and the investigation of model grafting to incorporate diverse data types seamlessly. Moreover, addressing bias, ensuring fairness, and enhancing interpretability are underscored as vital considerations for future research.

Conclusion

This comprehensive survey underscores the potential of LLMs in transcending the boundaries of natural language understanding to contribute significantly to tabular data modeling tasks. By systematically evaluating methodologies, identifying challenges, and recommending research directions, the survey equips researchers and practitioners with insights crucial for harnessing LLMs in this promising intersection of AI and data science. As LLMs continue to evolve, their integration with tabular data applications stands to revolutionize data analysis, prediction, and reasoning tasks, heralding a new era of AI-driven insights across various domains.