7 citations · 9 across the 7 of their papers we have counts for
8 papers
Claim-Level Confidence Calibration for Reliable Decision Making with Large Language Models
Toghrul Abbasli, Kentaroh Toyoda, Yuan Wang +1
Large Language Models (LLMs) increasingly support decision-making in high-stakes domains, but they often hallucinate and express confidence that is misaligned with factual correctn…
Improving Learning of New Diseases through Knowledge-Enhanced Initialization for Federated Adapter Tuning
Danni Peng, Yuan Wang, Kangning Cai +6
In healthcare, federated learning (FL) is a widely adopted framework that enables privacy-preserving collaboration among medical institutions. With large foundation models (FMs) de…
Quasi-Orthogonality and Polysemy: The Limits of Quantum Analogy in Large Language Models
Karl Svozil
We analyze two geometric aspects of meaning-sensitive behavior in next-token models. First, high-dimensional geometry permits exponentially many nearly orthogonal feature direction…
AiRacleX: Automated Detection of Price Oracle Manipulations via LLM-Driven Knowledge Mining and Prompt Generation
Bo Gao, Yuan Wang, Qingsong Wei +3
Decentralized finance (DeFi) applications depend on accurate price oracles to ensure secure transactions, yet these oracles are highly vulnerable to manipulation, enabling attacker…
Maximizing Uncertainty for Federated learning via Bayesian Optimisation-based Model Poisoning
Marios Aristodemou, Xiaolan Liu, Yuan Wang +3
As we transition from Narrow Artificial Intelligence towards Artificial Super Intelligence, users are increasingly concerned about their privacy and the trustworthiness of machine…
Look Back for More: Harnessing Historical Sequential Updates for Personalized Federated Adapter Tuning
Danni Peng, Yuan Wang, Huazhu Fu +4
Personalized federated learning (PFL) studies effective model personalization to address the data heterogeneity issue among clients in traditional federated learning (FL). Existing…
Blockchain Data Analysis in the Era of Large-Language Models
Kentaroh Toyoda, Xiao Wang, Mingzhe Li +3
Blockchain data analysis is essential for deriving insights, tracking transactions, identifying patterns, and ensuring the integrity and security of decentralized networks. It play…
An Aggregation-Free Federated Learning for Tackling Data Heterogeneity
Yuan Wang, Huazhu Fu, Renuga Kanagavelu +3
The performance of Federated Learning (FL) hinges on the effectiveness of utilizing knowledge from distributed datasets. Traditional FL methods adopt an aggregate-then-adapt framew…