14 citations · 33 across the 22 of their papers we have counts for
5 papers · 1 filter
Learning the Signature of Memorization in Autoregressive Language Models
David Ilić, Kostadin Cvejoski, David Stanojević +1
All prior membership inference attacks for fine-tuned language models use hand-crafted heuristics (e.g., loss thresholding, Min-K\%, reference calibration), each bounded by the des…
Protecting Private Code in IDE Autocomplete using Differential Privacy
Evgeny Grigorenko, David Stanojević, David Ilić +2
Modern Integrated Development Environments (IDEs) increasingly leverage Large Language Models (LLMs) to provide advanced features like code autocomplete. While powerful, training t…
Control Models for In-IDE Code Completion
Aral de Moor, Yana Hrynevich, Hleb Badzeika +6
We introduce control models for LLM-powered code completion in JetBrains IDEs: ML classifiers which trigger inference and filter the generated suggestions to better align them with…
Domain-Adaptation through Synthetic Data: Fine-Tuning Large Language Models for German Law
Ali Hamza Bashir, Muhammad Rehan Khalid, Kostadin Cvejoski +7
Large language models (LLMs) often struggle in specialized domains such as legal reasoning due to limited expert knowledge, resulting in factually incorrect outputs or hallucinatio…
Powerful Training-Free Membership Inference Against Autoregressive Language Models
David Ilić, David Stanojević, Kostadin Cvejoski
Fine-tuned language models pose significant privacy risks, as they may memorize and expose sensitive information from their training data. Membership inference attacks (MIAs) provi…