5 papers
CauTion: Knowing When to Trust LLMs for Ensemble Causal Discovery
Bo Peng, Kaiwen Wu, Sirui Chen +3
Causal discovery from observational data remains challenging due to the fundamental limitations of purely statistical methods, such as statistical distinguishability within equival…
Can LLMs Fool Graph Learning? Exploring Universal Adversarial Attacks on Text-Attributed Graphs
Zihui Chen, Yuling Wang, Pengfei Jiao +4
Text-attributed graphs (TAGs) enhance graph learning by integrating rich textual semantics and topological context for each node. While boosting expressiveness, they also expose ne…
MedConsultBench: A Full-Cycle, Fine-Grained, Process-Aware Benchmark for Medical Consultation Agents
Chuhan Qiao, Jianghua Huang, Daxing Zhao +5
Current evaluations of medical consultation agents often prioritize outcome-oriented tasks, frequently overlooking the end-to-end process integrity and clinical safety essential fo…
Enhancing reliability in AI inference services: An empirical study on real production incidents
Bhala Ranganathan, Mickey Zhang, Kai Wu
Hyperscale large language model (LLM) inference places extraordinary demands on cloud systems, where even brief failures can translate into significant user and business impact. To…
Dialect Normalization using Large Language Models and Morphological Rules
Antonios Dimakis, John Pavlopoulos, Antonios Anastasopoulos
Natural language understanding systems struggle with low-resource languages, including many dialects of high-resource ones. Dialect-to-standard normalization attempts to tackle thi…