8 citations · 12 across the 6 of their papers we have counts for
6 papers
Investigating and Mitigating Object Hallucinations in Pretrained Vision-Language (CLIP) Models
Yufang Liu, Tao Ji, Changzhi Sun +2
Large Vision-Language Models (LVLMs) have achieved impressive performance, yet research has pointed out a serious issue with object hallucinations within these models. However, the…
LongHeads: Multi-Head Attention is Secretly a Long Context Processor
Yi Lu, Xin Zhou, Wei He +5
Large language models (LLMs) have achieved impressive performance in numerous domains but often struggle to process lengthy inputs effectively and efficiently due to limited length…
StepCoder: Improve Code Generation with Reinforcement Learning from Compiler Feedback
Shihan Dou, Yan Liu, Haoxiang Jia +14
The advancement of large language models (LLMs) has significantly propelled the field of code generation. Previous work integrated reinforcement learning (RL) with compiler feedbac…
MouSi: Poly-Visual-Expert Vision-Language Models
Xiaoran Fan, Tao Ji, Changhao Jiang +21
Current large vision-language models (VLMs) often encounter challenges such as insufficient capabilities of a single visual component and excessively long visual tokens. These issu…
Secrets of RLHF in Large Language Models Part II: Reward Modeling
Binghai Wang, Rui Zheng, Lu Chen +24
Reinforcement Learning from Human Feedback (RLHF) has become a crucial technology for aligning language models with human values and intentions, enabling models to produce more hel…
Evaluator for Emotionally Consistent Chatbots
Chenxiao Liu, Guanzhi Deng, Tao Ji +2
One challenge for evaluating current sequence- or dialogue-level chatbots, such as Empathetic Open-domain Conversation Models, is to determine whether the chatbot performs in an em…