# Latent Predictor Networks for Code Generation

arXiv: https://arxiv.org/abs/1603.06744 · HTML: https://mlsanity.com/paper/1603.06744/

## Key facts

- Cited by 73 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Mar 2016.

## Built on by (most cited first, 30 of 73)

- [Evaluating Large Language Models Trained on Code](https://mlsanity.com/paper/2107.03374/) (2021)
- [Program Synthesis with Large Language Models](https://mlsanity.com/paper/2108.07732/) (2021)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [Measuring Coding Challenge Competence With APPS](https://mlsanity.com/paper/2105.09938/) (2021)
- [Natural Language to Code Translation with Execution](https://mlsanity.com/paper/2204.11454/) (2022)
- [CURE: Code-Aware Neural Machine Translation for Automatic Program Repair](https://mlsanity.com/paper/2103.00073/) (2021)
- [Coder Reviewer Reranking for Code Generation](https://mlsanity.com/paper/2211.16490/) (2022)
- [Out of the BLEU: how should we assess quality of the Code Generation models?](https://mlsanity.com/paper/2208.03133/) (2022)
- [A Syntax-Guided Edit Decoder for Neural Program Repair](https://mlsanity.com/paper/2106.08253/) (2021)
- [Large Language Model-Aware In-Context Learning for Code Generation](https://mlsanity.com/paper/2310.09748/) (2023)
- [MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languages](https://mlsanity.com/paper/2203.08388/) (2022)
- [CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generation](https://mlsanity.com/paper/2311.08588/) (2023)
- [Automatic Programming: Large Language Models and Beyond](https://mlsanity.com/paper/2405.02213/) (2024)
- [Sequence-to-Sequence Learning with Latent Neural Grammars](https://mlsanity.com/paper/2109.01135/) (2021)
- [Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models](https://mlsanity.com/paper/2308.10462/) (2023)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [NLPerturbator: Studying the Robustness of Code LLMs to Natural Language Variations](https://mlsanity.com/paper/2406.19783/) (2024)
- [An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunities](https://mlsanity.com/paper/2501.13742/) (2025)
- [Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs](https://mlsanity.com/paper/2403.13271/) (2024)
- [Shellcode_IA32: A Dataset for Automatic Shellcode Generation](https://mlsanity.com/paper/2104.13100/) (2021)
- [Discovering Non-monotonic Autoregressive Orderings with Variational Inference](https://mlsanity.com/paper/2110.15797/) (2021)
- [Games for Artificial Intelligence Research: A Review and Perspectives](https://mlsanity.com/paper/2304.13269/) (2023)
- [Neural Program Generation Modulo Static Analysis](https://mlsanity.com/paper/2111.01633/) (2021)
- [KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repair](https://mlsanity.com/paper/2302.01857/) (2023)
- [Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Tasks](https://mlsanity.com/paper/2402.05650/) (2024)
- [Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Model](https://mlsanity.com/paper/2510.18165/) (2025)
- [Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generation](https://mlsanity.com/paper/2208.10091/) (2022)
- [Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generators](https://mlsanity.com/paper/2212.06008/) (2022)
- [Improving Tree-Structured Decoder Training for Code Generation via Mutual Learning](https://mlsanity.com/paper/2105.14796/) (2021)
- [Exploring Dynamic Selection of Branch Expansion Orders for Code Generation](https://mlsanity.com/paper/2106.00261/) (2021)

## Abstract (arXiv)

Many language generation tasks require the production of text conditioned on both structured and unstructured inputs. We present a novel neural network architecture which generates an output sequence conditioned on an arbitrary number of input functions. Crucially, our approach allows both the choice of conditioning context and the granularity of generation, for example characters or tokens, to be marginalised, thus permitting scalable and effective training. Using this framework, we address the problem of generating programming code from a mixed natural language and structured specification. We create two new data sets for this paradigm derived from the collectible trading card games Magic the Gathering and Hearthstone. On these, and a third preexisting corpus, we demonstrate that marginalising multiple predictors allows our model to outperform strong benchmarks.