VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs

ACL ARR 2026 January Submission7961 Authors

Keywords: Data Generation, Diversity, Determinantal point process

Abstract: Large language models (LLMs) are increasingly being used to generate synthetic datasets for the evaluation and training of downstream models. However, prior work has noted that such generated data lacks diversity. In this paper, we propose VOYAGER, a novel principled approach to generate diverse datasets. Our approach is iterative and directly optimizes a mathematical quantity that optimizes the diversity of the dataset using the machinery of determinantal point processes. Furthermore, our approach is training-free, applicable to closed-source models, and scalable. In addition to providing theoretical justification for the working of our method, we also demonstrate through comprehensive experiments that VOYAGER improves diversity by $1.5-3x$ compared to popular baseline approaches.

Paper Type: Long

Research Area: Machine Learning for NLP

Research Area Keywords: Generation, Machine Learning for NLP, Resources and Evaluation

Contribution Types: Data resources, Theory

Languages Studied: English

Submission Number: 7961