mlsanityData to 14 Oct 2026

Latent Predictor Networks for Code Generation

arXiv 1603.06744 ↗First posted 22 Mar 2016cs.CL cs.NEcited by 73

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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.

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

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 20; 2022: 19; 2023: 12; 2024: 11; 2025: 10; 2026: 1.

73 papers.

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  1. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  2. 2021Program Synthesis with Large Language Modelscited by 3,637
  3. 2022Competition-Level Code Generation with AlphaCodecited by 1,462
  4. 2021Measuring Coding Challenge Competence With APPScited by 902
  5. 2022Natural Language to Code Translation with Executioncited by 124
  6. 2021CURE: Code-Aware Neural Machine Translation for Automatic Program Repaircited by 86
  7. 2022Coder Reviewer Reranking for Code Generationcited by 84
  8. 2022Out of the BLEU: how should we assess quality of the Code Generation models?cited by 65
  9. 2021A Syntax-Guided Edit Decoder for Neural Program Repaircited by 57
  10. 2023Large Language Model-Aware In-Context Learning for Code Generationcited by 43
  11. 2022MCoNaLa: A Benchmark for Code Generation from Multiple Natural Languagescited by 41
  12. 2023CodeScope: An Execution-based Multilingual Multitask Multidimensional Benchmark for Evaluating LLMs on Code Understanding and Generationcited by 39
38 more
  1. 2024Automatic Programming: Large Language Models and Beyondcited by 36
  2. 2021Sequence-to-Sequence Learning with Latent Neural Grammarscited by 30
  3. 2023Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Modelscited by 28
  4. 2023Deep Learning for Code Intelligence: Survey, Benchmark and Toolkitcited by 22
  5. 2024NLPerturbator: Studying the Robustness of Code LLMs to Natural Language Variationscited by 20
  6. 2025An Empirical Study of Retrieval-Augmented Code Generation: Challenges and Opportunitiescited by 18
  7. 2024Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMscited by 17
  8. 2021Shellcode_IA32: A Dataset for Automatic Shellcode Generationcited by 16
  9. 2021Discovering Non-monotonic Autoregressive Orderings with Variational Inferencecited by 15
  10. 2023Games for Artificial Intelligence Research: A Review and Perspectivescited by 14
  11. 2021Neural Program Generation Modulo Static Analysiscited by 13
  12. 2023KNOD: Domain Knowledge Distilled Tree Decoder for Automated Program Repaircited by 13
  13. 2024Rocks Coding, Not Development--A Human-Centric, Experimental Evaluation of LLM-Supported SE Taskscited by 13
  14. 2025Saber: An Efficient Sampling with Adaptive Acceleration and Backtracking Enhanced Remasking for Diffusion Language Modelcited by 13
  15. 2022Incorporating Domain Knowledge through Task Augmentation for Front-End JavaScript Code Generationcited by 11
  16. 2022Who Evaluates the Evaluators? On Automatic Metrics for Assessing AI-based Offensive Code Generatorscited by 11
  17. 2021Improving Tree-Structured Decoder Training for Code Generation via Mutual Learningcited by 10
  18. 2021Exploring Dynamic Selection of Branch Expansion Orders for Code Generationcited by 10
  19. 2024Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approachcited by 10
  20. 2025Exploring Code Language Models for Automated HLS-based Hardware Generation: Benchmark, Infrastructure and Analysiscited by 10
  21. 2025Towards an Understanding of Context Utilization in Code Intelligencecited by 10
  22. 2022On the Effectiveness of Pretrained Models for API Learningcited by 9
  23. 2023Pitfalls in Language Models for Code Intelligence: A Taxonomy and Surveycited by 9
  24. 2021Toward Code Generation: A Survey and Lessons from Semantic Parsingcited by 8
  25. 2021Reading StackOverflow Encourages Cheating: Adding Question Text Improves Extractive Code Generationcited by 8
  26. 2021Variable Name Recovery in Decompiled Binary Code using Constrained Masked Language Modelingcited by 7
  27. 2022CodePAD: Sequence-based Code Generation with Pushdown Automatoncited by 7
  28. 2025Pragmatic Reasoning improves LLM Code Generationcited by 7
  29. 2021Code Generation from Natural Language with Less Prior and More Monolingual Datacited by 6
  30. 2021CGEMs: A Metric Model for Automatic Code Generation using GPT-3cited by 6
  31. 2023Natural Language Commanding via Program Synthesiscited by 6
  32. 2024mathbbUSCD: Improving Code Generation of LLMs by Uncertainty-Aware Selective Contrastive Decodingcited by 6
  33. 2021Embedding API Dependency Graph for Neural Code Generationcited by 5
  34. 2021Lyra: A Benchmark for Turducken-Style Code Generationcited by 5
  35. 2024Efficacy of Various Large Language Models in Generating Smart Contractscited by 4
  36. 2025RefleXGen:The unexamined code is not worth usingcited by 4
  37. 2021Text2App: A Framework for Creating Android Apps from Text Descriptionscited by 3
  38. 2022A Survey on Artificial Intelligence for Source Code: A Dialogue Systems Perspectivecited by 3

50 most cited of 73 All 73 (JSON)