3 papers
cs.CL2025
Rationales Are Not Silver Bullets: Measuring the Impact of Rationales on Model Performance and Reliability
Chiwei Zhu, Benfeng Xu, An Yang +4
Training language models with rationales augmentation has been shown to be beneficial in many existing works. In this paper, we identify that such a prevailing view does not hold c…
cs.CL2025
ExpertPrompting: Instructing Large Language Models to be Distinguished Experts
Benfeng Xu, An Yang, Junyang Lin +4
The answering quality of an aligned large language model (LLM) can be drastically improved if treated with proper crafting of prompts. In this paper, we propose ExpertPrompting to…
cs.CV2024
Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution
Peng Wang, Shuai Bai, Sinan Tan +16
We present the Qwen2-VL Series, an advanced upgrade of the previous Qwen-VL models that redefines the conventional predetermined-resolution approach in visual processing. Qwen2-VL…