most citedRephQA: Evaluating Readability of Large Language Models in Public Health Question Answering

1 citations · 1 across the 2 of their papers we have counts for

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6 papers

cs.CL20251 cited

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…

cs.NI2025

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…

cs.LG2025

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…

cs.LG2025

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…

cs.LG2025

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…

cs.NI2024

SANDWICH: Towards an Offline, Differentiable, Fully-Trainable Wireless Neural Ray-Tracing Surrogate

Yifei Jin, Ali Maatouk, Sarunas Girdzijauskas +3

Wireless ray-tracing (RT) is emerging as a key tool for three-dimensional (3D) wireless channel modeling, driven by advances in graphical rendering. Current approaches struggle to…