18 citations · 61 across the 51 of their papers we have counts for
4 papers · 1 filter
Guided Collaboration in Heterogeneous LLM-Based Multi-Agent Systems via Entropy-Based Understanding Assessment and Experience Retrieval
Linlin Wang, Tianqing Zhu, Laiqiao Qin +2
With recent breakthroughs in large language models (LLMs) for reasoning, planning, and complex task generation, artificial intelligence systems are transitioning from isolated sing…
New Emerged Security and Privacy of Pre-trained Model: a Survey and Outlook
Meng Yang, Tianqing Zhu, Chi Liu +3
Thanks to the explosive growth of data and the development of computational resources, it is possible to build pre-trained models that can achieve outstanding performance on variou…
The Frontier of Data Erasure: Machine Unlearning for Large Language Models
Youyang Qu, Ming Ding, Nan Sun +3
Large Language Models (LLMs) are foundational to AI advancements, facilitating applications like predictive text generation. Nonetheless, they pose risks by potentially memorizing…
Divide and Ensemble: Progressively Learning for the Unknown
Hu Zhang, Xin Shen, Heming Du +10
In the wheat nutrient deficiencies classification challenge, we present the DividE and EnseMble (DEEM) method for progressive test data predictions. We find that (1) test images ar…