Kotlin ML Pack: Technical Report
In this technical report, we present three novel datasets of Kotlin code: KStack, KStack-clean, and KExercises. We also describe the results of fine-tuning CodeLlama and DeepSeek models on this data. Additionally, we present a version of the HumanEval benchmark rewritten by human experts into Kotlin - both the solutions and the tests. Our results demonstrate that small, high-quality datasets (KStack-clean and KExercises) can significantly improve model performance on code generation tasks, achieving up to a 16-point increase in pass rate on the HumanEval benchmark. Lastly, we discuss potential future work in the field of improving language modeling for Kotlin, including the use of static analysis tools in the learning process and the introduction of more intricate and realistic benchmarks.
Code (0)
등록된 구현이 없습니다.
Tasks
Code GenerationHumanEvalLanguage ModelingLanguage ModellingSimilar Papers 제목 키워드 기반
A Mobile Application Front-End for Presenting Explainable AI Results in Diabetes Risk Estimation
Diabetes is a significant and continuously rising health challenge in Indonesia. Although many artificial intelligence (AI)-based health applications have been developed for early detection, most function as "black boxes…
Docling Technical Report
This technical report introduces Docling, an easy to use, self-contained, MIT-licensed open-source package for PDF document conversion. It is powered by state-of-the-art specialized AI models for layout analysis (DocLayN…
Case Study: Using AI-Assisted Code Generation In Mobile Teams
The aim of this study is to evaluate the performance of AI-assisted programming in actual mobile development teams that are focused on native mobile languages like Kotlin and Swift. The extensive case study involves 16 p…
Code GenerationManagementPackage for Fast ABC-Boost
This report presents the open-source package which implements the series of our boosting works in the past years. In particular, the package includes mainly three lines of techniques, among which the following two are al…
Multi-class ClassificationregressionMulti-Goal Reinforcement Learning environments for simulated Franka Emika Panda robot
This technical report presents panda-gym, a set Reinforcement Learning (RL) environments for the Franka Emika Panda robot integrated with OpenAI Gym. Five tasks are included: reach, push, slide, pick & place and stack. T…
Multi-Goal Reinforcement LearningOpenAI Gymreinforcement-learningReinforcement Learning (RL)