most citedOmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence

1 citations · 1 across the 4 of their papers we have counts for

collaborators

5 papers

cs.CL2025

SoT: Structured-of-Thought Prompting Guides Multilingual Reasoning in Large Language Models

Rui Qi, Zhibo Man, Yufeng Chen +3

Recent developments have enabled Large Language Models (LLMs) to engage in complex reasoning tasks through deep thinking. However, the capacity of reasoning has not been successful…

cs.IR2025

Boosting Data Utilization for Multilingual Dense Retrieval

Chao Huang, Fengran Mo, Yufeng Chen +5

Multilingual dense retrieval aims to retrieve relevant documents across different languages based on a unified retriever model. The challenge lies in aligning representations of di…

cs.IR2025

Adaptive Personalized Conversational Information Retrieval

Fengran Mo, Yuchen Hui, Yuxing Tian +5

Personalized conversational information retrieval (CIR) systems aim to satisfy users' complex information needs through multi-turn interactions by considering user profiles. Howeve…

cs.CL2025

Multilingual Collaborative Defense for Large Language Models

Hongliang Li, Jinan Xu, Gengping Cui +3

The robustness and security of large language models (LLMs) has become a prominent research area. One notable vulnerability is the ability to bypass LLM safeguards by translating h…

cs.AI20251 cited

OmniGeo: Towards a Multimodal Large Language Models for Geospatial Artificial Intelligence

Long Yuan, Fengran Mo, Kaiyu Huang +6

The rapid advancement of multimodal large language models (LLMs) has opened new frontiers in artificial intelligence, enabling the integration of diverse large-scale data types suc…