# A Survey on LLM-based Code Generation for Low-Resource and Domain-Specific Programming Languages

arXiv: https://arxiv.org/abs/2410.03981 · HTML: https://mlsanity.com/paper/2410.03981/

## Key facts

- Cited by 66 papers in the map (data through 14 Oct 2026).
- Builds on 42 of its 58 references that are in the map.
- First posted on arXiv 4 Oct 2024.

## Builds on (most cited first, 30 of 42)

- [PaLM: Scaling Language Modeling with Pathways](https://mlsanity.com/paper/2204.02311/) (2022)
- [Self-Instruct: Aligning Language Models with Self-Generated Instructions](https://mlsanity.com/paper/2212.10560/) (2022)
- [Competition-Level Code Generation with AlphaCode](https://mlsanity.com/paper/2203.07814/) (2022)
- [CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation](https://mlsanity.com/paper/2109.00859/) (2021)
- [Large Language Models for Software Engineering: A Systematic Literature Review](https://mlsanity.com/paper/2308.10620/) (2023)
- [A Systematic Evaluation of Large Language Models of Code](https://mlsanity.com/paper/2202.13169/) (2022)
- [ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language](https://mlsanity.com/paper/2012.13048/) (2020)
- [CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X](https://mlsanity.com/paper/2303.17568/) (2023)
- [Query-Key Normalization for Transformers](https://mlsanity.com/paper/2010.04245/) (2020)
- [LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers](https://mlsanity.com/paper/2310.15164/) (2023)
- [Compositional Generalization and Natural Language Variation: Can a Semantic Parsing Approach Handle Both?](https://mlsanity.com/paper/2010.12725/) (2020)
- [Large Language Models Meet NL2Code: A Survey](https://mlsanity.com/paper/2212.09420/) (2022)
- [Task-Oriented Dialogue as Dataflow Synthesis](https://mlsanity.com/paper/2009.11423/) (2020)
- [CERT: Continual Pre-Training on Sketches for Library-Oriented Code Generation](https://mlsanity.com/paper/2206.06888/) (2022)
- [Software Engineering for AI-Based Systems: A Survey](https://mlsanity.com/paper/2105.01984/) (2021)
- [SketchGen: Generating Constrained CAD Sketches](https://mlsanity.com/paper/2106.02711/) (2021)
- [Harnessing the Power of Large Language Models for Natural Language to First-Order Logic Translation](https://mlsanity.com/paper/2305.15541/) (2023)
- [Grammar Prompting for Domain-Specific Language Generation with Large Language Models](https://mlsanity.com/paper/2305.19234/) (2023)
- [When Language Model Meets Private Library](https://mlsanity.com/paper/2210.17236/) (2022)
- [DAVE: Deriving Automatically Verilog from English](https://mlsanity.com/paper/2009.01026/) (2020)
- [xCodeEval: A Large Scale Multilingual Multitask Benchmark for Code Understanding, Generation, Translation and Retrieval](https://mlsanity.com/paper/2303.03004/) (2023)
- [Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMs](https://mlsanity.com/paper/2308.09895/) (2023)
- [Language Models for Code Completion: A Practical Evaluation](https://mlsanity.com/paper/2402.16197/) (2024)
- [A Deep Dive into Large Language Models for Automated Bug Localization and Repair](https://mlsanity.com/paper/2404.11595/) (2024)
- [Data is all you need: Finetuning LLMs for Chip Design via an Automated design-data augmentation framework](https://mlsanity.com/paper/2403.11202/) (2024)
- [ChatGPT for Programming Numerical Methods](https://mlsanity.com/paper/2303.12093/) (2023)
- [Rome was Not Built in a Single Step: Hierarchical Prompting for LLM-based Chip Design](https://mlsanity.com/paper/2407.18276/) (2024)
- [CoDesc: A Large Code-Description Parallel Dataset](https://mlsanity.com/paper/2105.14220/) (2021)
- [IRCoder: Intermediate Representations Make Language Models Robust Multilingual Code Generators](https://mlsanity.com/paper/2403.03894/) (2024)
- [Evaluation of OpenAI Codex for HPC Parallel Programming Models Kernel Generation](https://mlsanity.com/paper/2306.15121/) (2023)

## Built on by (most cited first, 30 of 66)

- [A Systematic Survey on Large Language Models for Algorithm Design](https://mlsanity.com/paper/2410.14716/) (2024)
- [Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI](https://mlsanity.com/paper/2505.19443/) (2025)
- [GUI Agents for Continual Game Generation](https://mlsanity.com/paper/2605.28258/) (2026)
- [Large Language Models (LLMs) for Source Code Analysis: applications, models and datasets](https://mlsanity.com/paper/2503.17502/) (2025)
- [CentaurEval: Benchmarking Human-in-the-Loop Value in Agentic Coding](https://mlsanity.com/paper/2512.04111/) (2025)
- [MicroRemed: Benchmarking LLMs in Microservices Remediation](https://mlsanity.com/paper/2511.01166/) (2025)
- [Towards Formal Verification of LLM-Generated Code from Natural Language Prompts](https://mlsanity.com/paper/2507.13290/) (2025)
- [Large Language Model for Verilog Code Generation: Literature Review and the Road Ahead](https://mlsanity.com/paper/2512.00020/) (2025)
- [SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Science](https://mlsanity.com/paper/2503.23314/) (2025)
- [Beyond the Prompt: An Empirical Study of Cursor Rules](https://mlsanity.com/paper/2512.18925/) (2025)
- [Uncovering Systematic Failures of LLMs in Verifying Code Against Natural Language Specifications](https://mlsanity.com/paper/2508.12358/) (2025)
- [KnapSpec: Self-Speculative Decoding via Adaptive Layer Selection as a Knapsack Problem](https://mlsanity.com/paper/2602.20217/) (2026)
- [Harnessing Language for Coordination: A Framework and Benchmark for LLM-Driven Multi-Agent Control](https://mlsanity.com/paper/2412.11761/) (2024)
- [Improving Existing Optimization Algorithms with LLMs](https://mlsanity.com/paper/2502.08298/) (2025)
- [TFHE-Coder: Evaluating LLM-agentic Fully Homomorphic Encryption Code Generation](https://mlsanity.com/paper/2503.12217/) (2025)
- [Understanding and Bridging the Planner-Coder Gap: A Systematic Study on the Robustness of Multi-Agent Systems for Code Generation](https://mlsanity.com/paper/2510.10460/) (2025)
- [Breaking the Static Graph: Context-Aware Traversal for Robust Retrieval-Augmented Generation](https://mlsanity.com/paper/2602.01965/) (2026)
- [Are LLMs Reliable Code Reviewers? Systematic Overcorrection in Requirement Conformance Judgement](https://mlsanity.com/paper/2603.00539/) (2026)
- [E2E-REME: Towards End-to-End Microservices Auto-Remediation via Experience-Simulation Reinforcement Fine-Tuning](https://mlsanity.com/paper/2604.11094/) (2026)
- [LLM-Supported Natural Language to Bash Translation](https://mlsanity.com/paper/2502.06858/) (2025)
- ["To Survive, I Must Defect": Jailbreaking LLMs via the Game-Theory Scenarios](https://mlsanity.com/paper/2511.16278/) (2025)
- [FlexLLM: Composable HLS Library for Flexible Hybrid LLM Accelerator Design](https://mlsanity.com/paper/2601.15710/) (2026)
- [Semantic Consensus Decoding: Backdoor Defense for Verilog Code Generation](https://mlsanity.com/paper/2602.04195/) (2026)
- [Towards Cold-Start Drafting and Continual Refining: A Value-Driven Memory Approach with Application to NPU Kernel Synthesis](https://mlsanity.com/paper/2603.10846/) (2026)
- [Large Language Models for Water Distribution Systems Modeling and Decision-Making](https://mlsanity.com/paper/2503.16191/) (2025)
- [Leveraging AI for Productive and Trustworthy HPC Software: Challenges and Research Directions](https://mlsanity.com/paper/2505.08135/) (2025)
- [Automating Structural Analysis Across Multiple Software Platforms Using Large Language Models](https://mlsanity.com/paper/2604.09866/) (2026)
- [Randomly Sampled Language Reasoning Problems Elucidate Limitations of In-Context Learning](https://mlsanity.com/paper/2501.02825/) (2025)
- [Using (Not-so) Large Language Models to Generate Simulation Models in a Formal DSL: A Study on Reaction Networks](https://mlsanity.com/paper/2503.01675/) (2025)
- [Evaluating the Process Modeling Abilities of Large Language Models -- Preliminary Foundations and Results](https://mlsanity.com/paper/2503.13520/) (2025)

## Abstract (arXiv)

Large Language Models (LLMs) have shown impressive capabilities in code generation for popular programming languages. However, their performance on Low-Resource Programming Languages (LRPLs) and Domain-Specific Languages (DSLs) remains a significant challenge, affecting millions of developers-3.5 million users in Rust alone-who cannot fully utilize LLM capabilities. LRPLs and DSLs encounter unique obstacles, including data scarcity and, for DSLs, specialized syntax that is poorly represented in general-purpose datasets. Addressing these challenges is crucial, as LRPLs and DSLs enhance development efficiency in specialized domains, such as finance and science. While several surveys discuss LLMs in software engineering, none focus specifically on the challenges and opportunities associated with LRPLs and DSLs. Our survey fills this gap by systematically reviewing the current state, methodologies, and challenges in leveraging LLMs for code generation in these languages. We filtered 111 papers from over 27,000 published studies between 2020 and 2024 to evaluate the capabilities and limitations of LLMs in LRPLs and DSLs. We report the LLMs used, benchmarks, and metrics for evaluation, strategies for enhancing performance, and methods for dataset collection and curation. We identified four main evaluation techniques and several metrics for assessing code generation in LRPLs and DSLs. Our analysis categorizes improvement methods into six groups and summarizes novel architectures proposed by researchers. Despite various techniques and metrics, a standard approach and benchmark dataset for evaluating code generation in LRPLs and DSLs are lacking. This survey serves as a resource for researchers and practitioners at the intersection of LLMs, software engineering, and specialized programming languages, laying the groundwork for future advancements in code generation for LRPLs and DSLs.