Główne pojęcia
The author explores using state-of-the-art LLMs to automatically generate tests for validating compiler implementations of OpenACC, focusing on code generation capabilities and prompt engineering techniques.
Streszczenie
The content discusses the use of LLMs like Codellama, Deepseek Coder, and GPT models to generate tests for OpenACC compiler validation. It highlights the challenges in compiler interpretation and the need for accurate validation tests. Various prompt engineering techniques are explored to improve test generation quality.
Large language models (LLMs) are utilized to automatically generate tests for validating OpenACC compiler implementations. The content emphasizes the importance of accurate validation tests due to differing interpretations by compiler developers. Different prompt engineering techniques are employed to enhance the quality of test generation.
Key points include:
- Use of LLMs like Codellama, Deepseek Coder, and GPT models for generating tests.
- Challenges in compiler interpretation leading to misinterpretations.
- Importance of accurate validation tests.
- Exploration of various prompt engineering techniques.
Statystyki
Large language models (LLMs) like GPT-4-Turbo produced passing tests.
Phind-Codellama-34b-v2 achieved a high score on benchmarks.
Deepseek-Coder-33b-Instruct showed competitive performance against GPT models.
Cytaty
"The goal is to check for correctness of C/C++/Fortran compiler implementations."
"LLMs require oversight but can be handy if the effort is front-loaded."
"Prompt engineering is a powerful method to adapt a model to specific tasks."