5 papers
Exploring Large Language Models for Detecting Mental Disorders
Gleb Kuzmin, Petr Strepetov, Maksim Stankevich +3
This paper compares the effectiveness of traditional machine learning methods, encoder-based models, and large language models (LLMs) on the task of detecting depression and anxiet…
RLBenchNet: The Right Network for the Right Reinforcement Learning Task
Ivan Smirnov, Shangding Gu
Reinforcement learning (RL) has seen significant advancements through the application of various neural network architectures. In this study, we systematically investigate the perf…
LAMeD: LLM-generated Annotations for Memory Leak Detection
Ekaterina Shemetova, Ilya Shenbin, Ivan Smirnov +5
Static analysis tools are widely used to detect software bugs and vulnerabilities but often struggle with scalability and efficiency in complex codebases. Traditional approaches re…
Inference-Time Selective Debiasing to Enhance Fairness in Text Classification Models
Gleb Kuzmin, Neemesh Yadav, Ivan Smirnov +2
We propose selective debiasing -- an inference-time safety mechanism designed to enhance the overall model quality in terms of prediction performance and fairness, especially in sc…
Navigating Ethical Challenges in Generative AI-Enhanced Research: The ETHICAL Framework for Responsible Generative AI Use
Douglas Eacersall, Lynette Pretorius, Ivan Smirnov +12
The rapid adoption of generative artificial intelligence (GenAI) in research presents both opportunities and ethical challenges that should be carefully navigated. Although GenAI t…