1 citations · 1 across the 4 of their papers we have counts for
10 papers
DRIFT: Refining Instruction Data via On-Policy Data Attribution
Zefan Wang, Lincheng Li, Tianyu Yu +1
Optimizing the training data distribution for Supervised Fine-Tuning (SFT) dictates the capability of Large Language Models (LLMs). While existing data curation methods excel at ac…
Deep Pre-Alignment for VLMs
Tianyu Yu, Kechen Fang, Zihao Wan +5
Most Vision Language Models (VLMs) directly map outputs from ViT encoders to the LLM via a lightweight projector. While effective, recent analysis suggests this architecture suffer…
LLaVA-UHD v4: What Makes Efficient Visual Encoding in MLLMs?
Kechen Fang, Yihua Qin, Chongyi Wang +3
Visual encoding constitutes a major computational bottleneck in Multimodal Large Language Models (MLLMs), especially for high-resolution image inputs. The prevailing practice typic…
MiniCPM-o 4.5: Towards Real-Time Full-Duplex Omni-Modal Interaction
Junbo Cui, Bokai Xu, Chongyi Wang +33
Recent progress in multimodal large language models (MLLMs) has brought AI capabilities from static offline data processing to real-time streaming interaction, yet they still remai…
LLaVA-UHD v3: Progressive Visual Compression for Efficient Native-Resolution Encoding in MLLMs
Shichu Sun, Yichen Zhang, Haolin Song +6
Visual encoding followed by token condensing has become the standard architectural paradigm in multi-modal large language models (MLLMs). Many recent MLLMs increasingly favor globa…
RLAIF-V: Open-Source AI Feedback Leads to Super GPT-4V Trustworthiness
Tianyu Yu, Haoye Zhang, Qiming Li +13
Traditional feedback learning for hallucination reduction relies on labor-intensive manual labeling or expensive proprietary models. This leaves the community without foundational…