mlsanityData to 14 Oct 2026

Data Race Detection Using Large Language Models

arXiv 2308.07505 ↗First posted 15 Aug 2023cs.LG cs.CLcited by 11

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Abstract (arXiv)

Large language models (LLMs) are demonstrating significant promise as an alternate strategy to facilitate analyses and optimizations of high-performance computing programs, circumventing the need for resource-intensive manual tool creation. In this paper, we explore a novel LLM-based data race detection approach combining prompting engineering and fine-tuning techniques. We create a dedicated dataset named DRB-ML, which is derived from DataRaceBench, with fine-grain labels showing the presence of data race pairs and their associated variables, line numbers, and read/write information. DRB-ML is then used to evaluate representative LLMs and fine-tune open-source ones. Our experiment shows that LLMs can be a viable approach to data race detection. However, they still cannot compete with traditional data race detection tools when we need detailed information about variable pairs causing data races.

Key facts. Cited by 11 papers in the map (data through 14 Oct 2026). Builds on 19 of its 40 references that are in the map. First posted on arXiv 15 Aug 2023.

Builds on

19 of 40 references.

References · 19

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  4. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  5. 2023QLoRA: Efficient Finetuning of Quantized LLMscited by 3,418
  6. 2023A Survey of Large Language Modelscited by 2,576
  7. 2019Fine-Tuning Language Models from Human Preferencescited by 2,150
  8. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  9. 2022Large Language Models Are Human-Level Prompt Engineerscited by 1,045
  10. 2021CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generationcited by 906
  11. 2023StarCoder: may the source be with you!cited by 799
  12. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 688
7 more
  1. 2023Sentiment Analysis in the Era of Large Language Models: A Reality Checkcited by 268
  2. 2022Large Language Models Meet NL2Code: A Surveycited by 138
  3. 2023Large language models effectively leverage document-level context for literary translation, but critical errors persistcited by 80
  4. 2023PanGu-Coder2: Boosting Large Language Models for Code with Ranking Feedbackcited by 69
  5. 2023LM4HPC: Towards Effective Language Model Application in High-Performance Computingcited by 18
  6. 2023Learning to Parallelize with OpenMP by Augmented Heterogeneous AST Representationcited by 14
  7. 2022Finding Reusable Machine Learning Components to Build Programming Language Processing Pipelinescited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2023: 3; 2024: 5; 2025: 2; 2026: 1.

11 papers.

  1. 2023Knowledge Transfer from High-Resource to Low-Resource Programming Languages for Code LLMscited by 45
  2. 2023HPC-GPT: Integrating Large Language Model for High-Performance Computingcited by 22
  3. 2024The Landscape and Challenges of HPC Research and LLMscited by 22
  4. 2024Performance-Aligned LLMs for Generating Fast Codecited by 21
  5. 2024OMPGPT: A Generative Pre-trained Transformer Model for OpenMPcited by 16
  6. 2025Leveraging Explainable AI for LLM Text Attribution: Differentiating Human-Written and Multiple LLMs-Generated Textcited by 11
  7. 2023CloudEval-YAML: A Practical Benchmark for Cloud Configuration Generationcited by 8
  8. 2024HPC-Coder-V2: Studying Code LLMs Across Low-Resource Parallel Languagescited by 8
  9. 2025Credit Risk Identification in Supply Chains Using Generative Adversarial Networkscited by 5
  10. 2024Can Large Language Model Predict Employee Attrition?cited by 3
  11. 2026CodegenBench: Can LLMs Write Efficient Code Across Architectures?cited by 0