An Automatic Prompt Generation System for Tabular Data Tasks
Ashlesha Akella, Abhijit Manatkar, Brij Chavda, Hima Patel

TL;DR
This paper introduces an automatic prompt generation system for tabular data tasks that leverages reinforcement learning and similarity-based methods to improve LLM performance across multiple datasets and tasks.
Contribution
It presents a novel auto-prompt generation system with minimal training, specifically designed for tabular data, using reinforcement learning and similarity-based techniques.
Findings
Improved performance on data imputation, error detection, and entity matching
Effective across 66 datasets and multiple LLMs
Demonstrates the system's versatility and robustness
Abstract
Efficient processing of tabular data is important in various industries, especially when working with datasets containing a large number of columns. Large language models (LLMs) have demonstrated their ability on several tasks through carefully crafted prompts. However, creating effective prompts for tabular datasets is challenging due to the structured nature of the data and the need to manage numerous columns. This paper presents an innovative auto-prompt generation system suitable for multiple LLMs, with minimal training. It proposes two novel methods; 1) A Reinforcement Learning-based algorithm for identifying and sequencing task-relevant columns 2) Cell-level similarity-based approach for enhancing few-shot example selection. Our approach has been extensively tested across 66 datasets, demonstrating improved performance in three downstream tasks: data imputation, error detection,…
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Taxonomy
TopicsParallel Computing and Optimization Techniques · Intelligent Tutoring Systems and Adaptive Learning · Advanced Database Systems and Queries
