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Found 2 papers, 1 papers with code
LLMParser: An Exploratory Study on Using Large Language Models for Log Parsing
Our evaluation of 16 open-source systems shows that LLMParser achieves statistically significantly higher parsing accuracy than state-of-the-art parsers (a 96% average parsing accuracy).
When LLM-based Code Generation Meets the Software Development Process
Results indicate LCGScrum outperforms other models, achieving Pass@1 scores of 75. 2, 65. 5, 82. 5, and 56. 7 in HumanEval, HumanEval-ET, MBPP, and MBPP-ET, respectively - an average 15% improvement over GPT.
Ranked #6 on
Code Generation
on MBPP
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