2 citations · 6 across the 15 of their papers we have counts for
7 papers · 1 filter
RephQA: Evaluating Readability of Large Language Models in Public Health Question Answering
Weikang Qiu, Tinglin Huang, Ryan Rullo +4
Large Language Models (LLMs) hold promise in addressing complex medical problems. However, while most prior studies focus on improving accuracy and reasoning abilities, a significa…
TelecomTS: A Multi-Modal Observability Dataset for Time Series and Language Analysis
Austin Feng, Andreas Varvarigos, Ioannis Panitsas +7
Modern enterprises generate vast streams of time series metrics when monitoring complex systems, known as observability data. Unlike conventional time series from domains such as c…
Agoran: An Agentic Open Marketplace for 6G RAN Automation
Ilias Chatzistefanidis, Navid Nikaein, Andrea Leone +5
Next-generation mobile networks must reconcile the often-conflicting goals of multiple service owners. However, today's network slice controllers remain rigid, policy-bound, and un…
TRACE: Grounding Time Series in Context for Multimodal Embedding and Retrieval
Jialin Chen, Ziyu Zhao, Gaukhar Nurbek +5
The ubiquity of dynamic data in domains such as weather, healthcare, and energy underscores a growing need for effective interpretation and retrieval of time-series data. These dat…
HELM: Hyperbolic Large Language Models via Mixture-of-Curvature Experts
Neil He, Rishabh Anand, Hiren Madhu +5
Large language models (LLMs) have shown great success in text modeling tasks across domains. However, natural language exhibits inherent semantic hierarchies and nuanced geometric…
Position: Beyond Euclidean -- Foundation Models Should Embrace Non-Euclidean Geometries
Neil He, Jiahong Liu, Buze Zhang +6
In the era of foundation models and Large Language Models (LLMs), Euclidean space has been the de facto geometric setting for machine learning architectures. However, recent litera…
MTBench: A Multimodal Time Series Benchmark for Temporal Reasoning and Question Answering
Jialin Chen, Aosong Feng, Ziyu Zhao +7
Understanding the relationship between textual news and time-series evolution is a critical yet under-explored challenge in applied data science. While multimodal learning has gain…