11 citations · 23 across the 15 of their papers we have counts for
9 papers · 1 filter
Who Can Withstand Chat-Audio Attacks? An Evaluation Benchmark for Large Audio-Language Models
Wanqi Yang, Yanda Li, Meng Fang +2
Adversarial audio attacks pose a significant threat to the growing use of large audio-language models (LALMs) in voice-based human-machine interactions. While existing research foc…
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…
Enhancing Temporal Sensitivity and Reasoning for Time-Sensitive Question Answering
Wanqi Yang, Yanda Li, Meng Fang +1
Time-Sensitive Question Answering (TSQA) demands the effective utilization of specific temporal contexts, encompassing multiple time-evolving facts, to address time-sensitive quest…
AppAgent v2: Advanced Agent for Flexible Mobile Interactions
Yanda Li, Chi Zhang, Wenjia Jiang +6
With the advancement of Multimodal Large Language Models (MLLM), LLM-driven visual agents are increasingly impacting software interfaces, particularly those with graphical user int…
Continual Learning for Temporal-Sensitive Question Answering
Wanqi Yang, Yunqiu Xu, Yanda Li +3
In this study, we explore an emerging research area of Continual Learning for Temporal Sensitive Question Answering (CLTSQA). Previous research has primarily focused on Temporal Se…
Adaptive Reinforcement Learning Planning: Harnessing Large Language Models for Complex Information Extraction
Zepeng Ding, Ruiyang Ke, Wenhao Huang +4
Existing research on large language models (LLMs) shows that they can solve information extraction tasks through multi-step planning. However, their extraction behavior on complex…
Tokenization Matters! Degrading Large Language Models through Challenging Their Tokenization
Dixuan Wang, Yanda Li, Junyuan Jiang +5
Large Language Models (LLMs) have shown remarkable capabilities in language understanding and generation. Nonetheless, it was also witnessed that LLMs tend to produce inaccurate re…
Reason from Fallacy: Enhancing Large Language Models' Logical Reasoning through Logical Fallacy Understanding
Yanda Li, Dixuan Wang, Jiaqing Liang +4
Large Language Models (LLMs) have demonstrated good performance in many reasoning tasks, but they still struggle with some complicated reasoning tasks including logical reasoning.…
MMAC-Copilot: Multi-modal Agent Collaboration Operating Copilot
Zirui Song, Yaohang Li, Meng Fang +6
Large language model agents that interact with PC applications often face limitations due to their singular mode of interaction with real-world environments, leading to restricted…