3 citations · 5 across the 11 of their papers we have counts for
15 papers
DISC: Domain-Integrated Similarity Cost for Time-Series Dataset
Yen-Ku Liu, Hongjie Chen, Franck Dernoncourt +1
The growth of time-series datasets and foundation models has made dataset comparison a central part of time-series workflows. Assessing source-target transfer before model adaptati…
FlexRouter: Learning Complementary Model Sets for Flexible LLM Routing
Wang Wei, Harry Yang, Tiankai Yang +6
Existing Large Language Model (LLM) routing methods score LLMs independently to select top- models. However, this ignores model correlations and enforces a rigid computational b…
Controlled Decoding Attacks on Black-Box LLMs
Jesson Wang, Shawn Li, Wei Yang +4
Manipulating next-token probabilities during generation can bypass the safety alignment of large language models. Existing approaches, however, rely on access to model weights or n…
Beyond Top- Skill Retrieval: Diversity-Aware Skill Routing for LLM Agents
Wang Wei, Tiankai Yang, Samyadeep Basu +7
Large language model (LLM) agents increasingly rely on external skills, but routing user requests over large skill registries is difficult because many skills are functionally redu…
TSDS-Toolbox: A Toolbox for Measuring Time-Series Dataset Similarity
Yen-Ku Liu, Hongjie Chen, Ryan A. Rossi +1
The rapid advancement of artificial intelligence (AI) has significantly accelerated research in time-series analysis, particularly in forecasting, classification, and generation ta…
No Attacker Needed: Unintentional Cross-User Contamination in Shared-State LLM Agents
Tiankai Yang, Jiate Li, Yi Nian +5
LLM-based agents increasingly operate across repeated sessions, maintaining task states to ensure continuity. In many deployments, a single agent serves multiple users within a tea…
Variable-Length Audio Fingerprinting
Hongjie Chen, Hanyu Meng, Huimin Zeng +3
Audio fingerprinting converts audio to much lower-dimensional representations, allowing distorted recordings to still be recognized as their originals through similar fingerprints.…
"Someone Hid It": Query-Agnostic Black-Box Attacks on LLM-Based Retrieval
Jiate Li, Defu Cao, Li Li +8
Large language models (LLMs) have been serving as effective backbones for retrieval systems, including Retrieval-Augmentation-Generation (RAG), Dense Information Retriever (IR), an…
Learning to Route LLMs from Bandit Feedback: One Policy, Many Trade-offs
Wang Wei, Tiankai Yang, Hongjie Chen +4
Efficient use of large language models (LLMs) is critical for deployment at scale: without adaptive routing, systems either overpay for strong models or risk poor performance from…
Measuring Time-Series Dataset Similarity using Wasserstein Distance
Hongjie Chen, Akshay Mehra, Josh Kimball +1
The emergence of time-series foundation model research elevates the growing need to measure the (dis)similarity of time-series datasets. A time-series dataset similarity measure ai…
A Personalized Conversational Benchmark: Towards Simulating Personalized Conversations
Li Li, Peilin Cai, Ryan A. Rossi +21
We present PersonaConvBench, a large-scale benchmark for evaluating personalized reasoning and generation in multi-turn conversations with large language models (LLMs). Unlike exis…
Generative AI for Autonomous Driving: Frontiers and Opportunities
Yuping Wang, Shuo Xing, Cui Can +46
Generative Artificial Intelligence (GenAI) constitutes a transformative technological wave that reconfigures industries through its unparalleled capabilities for content creation,…
Few-Shot Graph Out-of-Distribution Detection with LLMs
Haoyan Xu, Zhengtao Yao, Yushun Dong +4
Existing methods for graph out-of-distribution (OOD) detection typically depend on training graph neural network (GNN) classifiers using a substantial amount of labeled in-distribu…
Political-LLM: Large Language Models in Political Science
Lincan Li, Jiaqi Li, Catherine Chen +44
In recent years, large language models (LLMs) have been widely adopted in political science tasks such as election prediction, sentiment analysis, policy impact assessment, and mis…
AD-LLM: Benchmarking Large Language Models for Anomaly Detection
Tiankai Yang, Yi Nian, Shawn Li +9
Anomaly detection (AD) is an important machine learning task with many real-world uses, including fraud detection, medical diagnosis, and industrial monitoring. Within natural lang…