Large Language Models (LLMs) are increasingly explored for time series anomaly detection (TSAD), yet their direct use often remains inferior to methods designed specifically for TSAD. In this work, we study whether this limitation arises primarily from weak forecasting or from insufficient exploitation of forecast resi...
Hakan Akdag, A. T. Bui, Natalie Hußfeldt et al.· Proceedings of the 35th ACM...· 0 citations
Large Language Models (LLMs) are increasingly explored for time series anomaly detection (TSAD), yet their direct use often remains inferior to methods designed specifically for TSAD. In this work, we study whether this limitation arises primarily from weak forecasting or from insufficient exploitation of forecast resi...
Hakan Akdag, A. T. Bui, Natalie Hußfeldt et al.· Proceedings of the 35th ACM...· 0 citations
Probing the neurons associated with security-relevant knowledge helps to gain insights into how LLMs encode malicious programming concepts, identify potentially harmful memorized behaviors, and pave the way toward more reliable defense mechanisms, such as neuron-level editing, selective unlearning, and security-aware a...
Lam D. Dao, V. T. Nguyễn, Anh M. T. Bui et al.· arXiv.org· 0 citations
XREPOTEST is introduced, a multilingual repository-level benchmark for unit test generation spanning five underexplored languages: Rust, Go, Julia, PHP, and Ruby, and Invocation Rate is proposed to assess whether generated tests meaningfully exercise the intended functionality.
L. Dung, Dong Cao Van, Nam Le Hai et al.· 1 citation
CURE is developed, a contrastive unlearning approach that shifts unlearning from purely suppressing outdated knowledge to explicitly promoting correct API replacements, enabling more reliable adaptation to evolving software libraries.
Huy Q. Tran, Dang H. Vu, Tuyen N. Dinh et al.· arXiv.org· 0 citations
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