Summary of Explicit Diversity Conditions For Effective Question Answer Generation with Large Language Models, by Vikas Yadav and Hyuk Joon Kwon and Vijay Srinivasan and Hongxia Jin
Explicit Diversity Conditions for Effective Question Answer Generation with Large Language Models
by Vikas Yadav, Hyuk Joon Kwon, Vijay Srinivasan, Hongxia Jin
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 This research paper presents a new approach to improving question answering systems, specifically focusing on generating diverse synthetic data using Question Answer Generation (QAG) techniques. The authors highlight the issue of redundant QA pair generation in current methods and propose explicit diversity conditions for QAG, which significantly increase diversity in generated question-answer pairs. These conditions focus on spatial aspects, question types, and entities. The paper shows that using these explicit diversity conditions results in an average 4.1% exact match and 4.5% F1 improvement over existing implicit diversity techniques, such as sampling and diverse beam search. The authors also emphasize the importance of explicit diversity conditions for generating diverse synthetic data, especially in low-resource datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In a nutshell, this research paper develops a new way to generate question-answer pairs that helps improve question answering systems. It tackles a problem where current methods often repeat similar questions and answers. The solution is simple: introduce more variety by focusing on different aspects of the question and answer. This makes the generated data more diverse and useful for training these systems. The results show that this approach works well, especially when dealing with limited data. |
Keywords
» Artificial intelligence » Question answering » Synthetic data
