Fine-tuning LLaMA 3B with LoRA on synthetic Solidity contracts yields 67% accuracy on the contracts it chooses to classify, but only about 52% when abstentions are counted as errors.
In: Fundamentals of Software Engineering: 8th International Conference, FSEN 2019, Tehran, Iran, May 1-3, 2019, Revised Selected Papers 8, pp
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.SE 1years
2025 1verdicts
REJECT 1roles
background 1polarities
unclear 1representative citing papers
citing papers explorer
-
An Initial Exploration of Fine-tuning Small Language Models for Smart Contract Reentrancy Vulnerability Detection
Fine-tuning LLaMA 3B with LoRA on synthetic Solidity contracts yields 67% accuracy on the contracts it chooses to classify, but only about 52% when abstentions are counted as errors.