Summary of An Automatic Prompt Generation System For Tabular Data Tasks, by Ashlesha Akella et al.
An Automatic Prompt Generation System for Tabular Data Tasks
by Ashlesha Akella, Abhijit Manatkar, Brij Chavda, Hima Patel
First submitted to arxiv on: 9 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed auto-prompt generation system for large language models (LLMs) tackles the challenge of processing tabular data with a large number of columns. By presenting two novel methods, the system aims to improve performance in downstream tasks such as data imputation, error detection, and entity matching. Specifically, the paper introduces a Reinforcement Learning-based algorithm for identifying and sequencing task-relevant columns, as well as a Cell-level similarity-based approach for enhancing few-shot example selection. The system is tested across 66 datasets using two distinct LLMs, Google flan-t5-xxl and Mixtral 8x7B. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models (LLMs) are great at processing text data, but when it comes to tabular data with many columns, they need help. The problem is creating the right prompts for these datasets. This paper presents a new way to do just that using two special methods. One method helps identify which columns are most important for a task, and the other helps choose the best examples from the dataset. The system was tested on 66 different datasets and showed improved results in three tasks: filling in missing data, finding errors, and matching up entities. |
Keywords
» Artificial intelligence » Few shot » Prompt » Reinforcement learning » T5
