datagen

David J. Lee, 2022-2024

Synthesize novel and diverse datasets for low-resource program synthesis domains.

Domains

• Lindenmayer systems
• Mujoco/2D ant walker programs
• Python programming puzzles
• Regular expressions

Samples

Lindenmayer systems

Ant walker paths

Programming problems

See examples/puzzles

Framework

Treat dataset generation as Markov chain Monte Carlo search over sets of candidate programs, with the following proposal distributions:

• LLM: generate new programs via prompting an LLM with program mutation prompts (Python puzzles)
• PCFG: fit a probabilistic context-free grammar (PCFG) to a program or set of programs using inside-outside, and then sample from the fitted grammar

and the following target distributions:

• energy: maximize a physically motivated "energy" function over program embeddings (pulled from an off-the-shelf embedding model and compressed using dimensionality reduction techniques when necessary)
• variance: maximize the sum of embedding distances from the mean embedding among other variations

Note: this is research code, so browse at your own peril. :)