VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs
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 significantly outperforms popular baseline approaches by providing a 1.5-3x improvement in diversity.
6941b5d65d5b2dc105274718
Avinash Amballa
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2025-12-16T22:32:37.221Z
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/avatars/897cb5bd4915d6ded23419d5d12623db.svg
Avinash Amballa
AvinashAmballa
user
AvinashAmballa
6941b5d65d5b2dc105274719
Yashas Malur Saidutta
6941b5d65d5b2dc10527471a
Chi-Heng Lin
6941b5d65d5b2dc10527471b
Vivek Kulkarni
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2025-12-19T09:07:47.355Z
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/avatars/7de659ec033dc96685c9e2bac9fa5e69.svg
Vivek Kulkarni
Vivek-kulkarni-samsung
user
Vivek-kulkarni-samsung
6941b5d65d5b2dc10527471c
Srinivas Chappidi
VOYAGER: A Training Free Approach for Generating Diverse Datasets using LLMs
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 significantly outperforms popular baseline approaches by providing a 1.5-3x improvement in diversity.
Voyager is a method that uses determinantal point processes to iteratively generate diverse synthetic datasets for model evaluation and training.
determinantal point processes