# Creating Synthetic Datasets via Evolution for Neural Program Synthesis

arXiv: https://arxiv.org/abs/2003.10485 · HTML: https://mlsanity.com/paper/2003.10485/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [Latent Execution for Neural Program Synthesis](https://mlsanity.com/paper/2107.00101/) (2021)
- [Synthesizing a Progression of Subtasks for Block-Based Visual Programming Tasks](https://mlsanity.com/paper/2305.17518/) (2023)

## Abstract (arXiv)

Program synthesis is the task of automatically generating a program consistent with a given specification. A natural way to specify programs is to provide examples of desired input-output behavior, and many current program synthesis approaches have achieved impressive results after training on randomly generated input-output examples. However, recent work has discovered that some of these approaches generalize poorly to data distributions different from that of the randomly generated examples. We show that this problem applies to other state-of-the-art approaches as well and that current methods to counteract this problem are insufficient. We then propose a new, adversarial approach to control the bias of synthetic data distributions and show that it outperforms current approaches.