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Unlocking the Power of Tables with Large Language Models: A Comprehensive Survey on Automating Data-Intensive Tasks


Large Language Models(LLMs) have been successful in processing data in the form of text, images, and audio, but they face challenges when the data is provided as a table. LLMs can work better on a variety of tasks, such as database queries, spreadsheet calculations, and generating reports from web tables if the model can interpret the tables as input. A team of researchers from the Renmin University of China addresses the challenge of automating table-related tasks by focusing on instruction-tuning, prompting, and agent-based approaches in the context of LLMs. 

Current methods primarily involve training or fine-tuning LLMs for specific table tasks, but these methods often need more robustness and are unable to generalize well to unseen tasks. The proposed solution in the paper introduces instruction tuning, prompting, and agent-based approaches within the realm of LLMs to address these limitations.

Let’s see how the three key features help in interpreting the table:

  1. Instruction tuning involves fine-tuning LLMs on a collection of datasets, offering higher flexibility and improved performance on unseen tasks. 
  2. Prompting techniques aim to convert tables into prompts while maintaining their semantic integrity. This enables LLMs to process table data effectively.
  3. Finally, the agent-based approach supports LLMs in interacting with external tools and performing complex table tasks through iterative observation, action planning, and reflection.

These methods demonstrate promising results in terms of accuracy and efficiency, although they may require substantial computational resources and careful dataset curation.

With this paper, researchers tried to provide a functional approach to address the challenges of automating table-related tasks using LLMs. Using their proposed approach, researchers demonstrated significant improvements in the accuracy and efficiency of LLMs across various table tasks. The performance of the approach is reliable, but there are a number of drawbacks — computationally expensive, dataset curation, and generalizability.

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