1 citations · 1 across the 1 of their papers we have counts for
8 papers
Kick Bad Guys Out! Conditionally Activated Anomaly Detection in Federated Learning with Zero-Knowledge Proof Verification
Shanshan Han, Wenxuan Wu, Baturalp Buyukates +4
Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations…
Don't Always Pick the Highest-Performing Model: An Information Theoretic View of LLM Ensemble Selection
Yigit Turkmen, Baturalp Buyukates, Melih Bastopcu
Large language models (LLMs) are often ensembled together to improve overall reliability and robustness, but in practice models are strongly correlated. This raises a fundamental q…
Uncertainty Quantification for Hallucination Detection in Large Language Models: Foundations, Methodology, and Future Directions
Sungmin Kang, Yavuz Faruk Bakman, Duygu Nur Yaldiz +2
The rapid advancement of large language models (LLMs) has transformed the landscape of natural language processing, enabling breakthroughs across a wide range of areas including qu…
Balancing Information Accuracy and Response Timeliness in Networked LLMs
Yigit Turkmen, Baturalp Buyukates, Melih Bastopcu
Recent advancements in Large Language Models (LLMs) have transformed many fields including scientific discovery, content generation, biomedical text mining, and educational technol…
Reconsidering LLM Uncertainty Estimation Methods in the Wild
Yavuz Bakman, Duygu Nur Yaldiz, Sungmin Kang +4
Large Language Model (LLM) Uncertainty Estimation (UE) methods have become a crucial tool for detecting hallucinations in recent years. While numerous UE methods have been proposed…
Privacy-preserving Prompt Personalization in Federated Learning for Multimodal Large Language Models
Sizai Hou, Songze Li, Baturalp Buyukates
Prompt learning is a crucial technique for adapting pre-trained multimodal language models (MLLMs) to user tasks. Federated prompt personalization (FPP) is further developed to add…