80 citations · 557 across the 56 of their papers we have counts for
11 papers · 1 filter
Adversarial Robust Memory-Based Continual Learner
Xiaoyue Mi, Fan Tang, Zonghan Yang +4
Despite the remarkable advances that have been made in continual learning, the adversarial vulnerability of such methods has not been fully discussed. We delve into the adversarial…
Filling the Image Information Gap for VQA: Prompting Large Language Models to Proactively Ask Questions
Ziyue Wang, Chi Chen, Peng Li +1
Large Language Models (LLMs) demonstrate impressive reasoning ability and the maintenance of world knowledge not only in natural language tasks, but also in some vision-language ta…
EgoThink: Evaluating First-Person Perspective Thinking Capability of Vision-Language Models
Sijie Cheng, Zhicheng Guo, Jingwen Wu +4
Vision-language models (VLMs) have recently shown promising results in traditional downstream tasks. Evaluation studies have emerged to assess their abilities, with the majority fo…
Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
Zeyuan Yang, Peng Li, Yang Liu
Large Language Models (LLMs) have showcased impressive performance. However, due to their inability to capture relationships among samples, these frozen LLMs inevitably keep repeat…
Self-Knowledge Guided Retrieval Augmentation for Large Language Models
Yile Wang, Peng Li, Maosong Sun +1
Large language models (LLMs) have shown superior performance without task-specific fine-tuning. Despite the success, the knowledge stored in the parameters of LLMs could still be i…
A Dynamic LLM-Powered Agent Network for Task-Oriented Agent Collaboration
Zijun Liu, Yanzhe Zhang, Peng Li +2
Recent studies show that collaborating multiple large language model (LLM) powered agents is a promising way for task solving. However, current approaches are constrained by using…