# Transformer-based Program Synthesis for Low-Data Environments

arXiv: https://arxiv.org/abs/2205.09246 · HTML: https://mlsanity.com/paper/2205.09246/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 17 references that are in the map.
- First posted on arXiv 18 May 2022.

## Builds on (most cited first, 9 of 9)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [The Pile: An 800GB Dataset of Diverse Text for Language Modeling](https://mlsanity.com/paper/2101.00027/) (2020)
- [Show Your Work: Scratchpads for Intermediate Computation with Language Models](https://mlsanity.com/paper/2112.00114/) (2021)
- [DeepCoder: Learning to Write Programs](https://mlsanity.com/paper/1611.01989/) (2016)
- [RobustFill: Neural Program Learning under Noisy I/O](https://mlsanity.com/paper/1703.07469/) (2017)
- [Program Synthesis from Polymorphic Refinement Types](https://mlsanity.com/paper/1510.08419/) (2015)
- [Synthetic Datasets for Neural Program Synthesis](https://mlsanity.com/paper/1912.12345/) (2019)
- [Neural Program Generation Modulo Static Analysis](https://mlsanity.com/paper/2111.01633/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Survey on Large Language Models from Concept to Implementation](https://mlsanity.com/paper/2403.18969/) (2024)

## Abstract (arXiv)

Recent advancements in large pre-trained transformer models (GPT2/3, T5) have found use in program synthesis to generate programs that satisfy a set of input/output examples. However, these models perform poorly on long-horizon and low-data tasks, and often don't seem to understand the semantics of the languages they generate. We investigate an approach that tackles both of these issues, by using attributed context-free-grammars of programming languages to generate programs, and then analyzing generated programs so that they can be annotated with compile and runtime attributes, such as types, so that information about the program can be remembered during long-horizon generation. We firstly find that synthesized datasets can be made efficiently and can provide transformer models with enough data in order to perform well on some synthesis tasks. We also find that giving models access to program attributes is especially effective in low-data environments, and tends improve the quality and reduce errors of transformer-generated programs.