1 citations · 1 across the 5 of their papers we have counts for
6 papers
AutoRed: A Free-form Adversarial Prompt Generation Framework for Automated Red Teaming
Muxi Diao, Yutao Mou, Keqing He +6
The safety of Large Language Models (LLMs) is crucial for the development of trustworthy AI applications. Existing red teaming methods often rely on seed instructions, which limits…
RecGPT Technical Report
Chao Yi, Dian Chen, Gaoyang Guo +51
Recommender systems are among the most impactful applications of artificial intelligence, serving as critical infrastructure connecting users, merchants, and platforms. However, mo…
CareBot: A Pioneering Full-Process Open-Source Medical Language Model
Lulu Zhao, Weihao Zeng, Xiaofeng Shi +1
Recently, both closed-source LLMs and open-source communities have made significant strides, outperforming humans in various general domains. However, their performance in specific…
Smaller Language Models Are Better Instruction Evolvers
Tingfeng Hui, Lulu Zhao, Guanting Dong +3
Instruction tuning has been widely used to unleash the complete potential of large language models. Notably, complex and diverse instructions are of significant importance as they…
MoSLD: An Extremely Parameter-Efficient Mixture-of-Shared LoRAs for Multi-Task Learning
Lulu Zhao, Weihao Zeng, Xiaofeng Shi +1
Recently, LoRA has emerged as a crucial technique for fine-tuning large pre-trained models, yet its performance in multi-task learning scenarios often falls short. In contrast, the…
B-STaR: Monitoring and Balancing Exploration and Exploitation in Self-Taught Reasoners
Weihao Zeng, Yuzhen Huang, Lulu Zhao +3
In the absence of extensive human-annotated data for complex reasoning tasks, self-improvement -- where models are trained on their own outputs -- has emerged as a primary method f…