# Sample-efficient Linguistic Generalizations through Program Synthesis: Experiments with Phonology Problems

arXiv: https://arxiv.org/abs/2106.06566 · HTML: https://mlsanity.com/paper/2106.06566/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 11 Jun 2021.

## Builds on (most cited first, 5 of 5)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [SIGMORPHON 2020 Shared Task 0: Typologically Diverse Morphological Inflection](https://mlsanity.com/paper/2006.11572/) (2020)
- [PuzzLing Machines: A Challenge on Learning From Small Data](https://mlsanity.com/paper/2004.13161/) (2020)
- [A Neural Transducer](https://mlsanity.com/paper/1511.04868/) (2015)

## Built on by (most cited first, 4 of 4)

- [LingBench++: A Linguistically-Informed Benchmark and Reasoning Framework for Multi-Step and Cross-Cultural Inference with LLMs](https://mlsanity.com/paper/2507.16809/) (2025)
- [Programming by Examples Meets Historical Linguistics: A Large Language Model Based Approach to Sound Law Induction](https://mlsanity.com/paper/2501.16524/) (2025)
- [Can Large Language Models Code Like a Linguist?: A Case Study in Low Resource Sound Law Induction](https://mlsanity.com/paper/2406.12725/) (2024)
- [The Linguistics Olympiads: Towards a New Corpus for Linguistics Research?](https://mlsanity.com/paper/2606.14257/) (2026)

## Abstract (arXiv)

Neural models excel at extracting statistical patterns from large amounts of data, but struggle to learn patterns or reason about language from only a few examples. In this paper, we ask: Can we learn explicit rules that generalize well from only a few examples? We explore this question using program synthesis. We develop a synthesis model to learn phonology rules as programs in a domain-specific language. We test the ability of our models to generalize from few training examples using our new dataset of problems from the Linguistics Olympiad, a challenging set of tasks that require strong linguistic reasoning ability. In addition to being highly sample-efficient, our approach generates human-readable programs, and allows control over the generalizability of the learnt programs.