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20232025
most citedRetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering

1 citations · 1 across the 13 of their papers we have counts for

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cs.AI2024

Foundations and Recent Trends in Multimodal Mobile Agents: A Survey

Biao Wu, Yanda Li, Zhiwei Zhang +3

Mobile agents are essential for automating tasks in complex and dynamic mobile environments. As foundation models evolve, the demands for agents that can adapt in real-time and pro…

cs.CL2024

MedINST: Meta Dataset of Biomedical Instructions

Wenhan Han, Meng Fang, Zihan Zhang +5

The integration of large language model (LLM) techniques in the field of medical analysis has brought about significant advancements, yet the scarcity of large, diverse, and well-a…

cs.RO2024

Hazards in Daily Life? Enabling Robots to Proactively Detect and Resolve Anomalies

Zirui Song, Guangxian Ouyang, Meng Fang +9

Existing household robots have made significant progress in performing routine tasks, such as cleaning floors or delivering objects. However, a key limitation of these robots is th…

cs.CV2024

Motion Avatar: Generate Human and Animal Avatars with Arbitrary Motion

Zeyu Zhang, Yiran Wang, Biao Wu +7

In recent years, there has been significant interest in creating 3D avatars and motions, driven by their diverse applications in areas like film-making, video games, AR/VR, and hum…

cs.CL2024★ 1 cited

RetrievalQA: Assessing Adaptive Retrieval-Augmented Generation for Short-form Open-Domain Question Answering

Zihan Zhang, Meng Fang, Ling Chen

Adaptive retrieval-augmented generation (ARAG) aims to dynamically determine the necessity of retrieval for queries instead of retrieving indiscriminately to enhance the efficiency…