Towards Neural Functional Program Evaluation
Not on the map. Why?
Abstract (arXiv)
This paper explores the capabilities of current transformer-based language models for program evaluation of simple functional programming languages. We introduce a new program generation mechanism that allows control over syntactic sugar for semantically equivalent programs. T5 experiments reveal that neural functional program evaluation performs surprisingly well, achieving high 90% exact program match scores for most in-distribution and out-of-distribution tests. Using pretrained T5 weights has significant advantages over random initialization. We present and evaluate on three datasets to study generalization abilities that are specific to functional programs based on: type, function composition, and reduction steps. Code and data are publicly available at https://github.com/ElementAI/neural-interpreters.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 14 of its 20 references that are in the map. First posted on arXiv 9 Dec 2021.
Builds on
14 of 20 references.
References · 14
- 2017Attention Is All You Needcited by 60,361
- 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,599
- 2021Evaluating Large Language Models Trained on Codecited by 8,320
- 2021Program Synthesis with Large Language Modelscited by 3,637
- 2018Adafactor: Adaptive Learning Rates with Sublinear Memory Costcited by 905
- 2020DreamCoder: Growing generalizable, interpretable knowledge with wake-sleep Bayesian program learningcited by 161
- 2014Learning to Executecited by 106
- 2015Neural Programmer-Interpreterscited by 101
- 2020Compositional Generalization via Neural-Symbolic Stack Machinescited by 80
- 2020Learning Compositional Rules via Neural Program Synthesiscited by 74
- 2020Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networkscited by 35
- 2016Programming with a Differentiable Forth Interpretercited by 33
Built on by
Not cited yet.