25 citations · 91 across the 19 of their papers we have counts for
8 papers · 1 filter
Florence-VL: Enhancing Vision-Language Models with Generative Vision Encoder and Depth-Breadth Fusion
Jiuhai Chen, Jianwei Yang, Haiping Wu +4
We present Florence-VL, a new family of multimodal large language models (MLLMs) with enriched visual representations produced by Florence-2, a generative vision foundation model.…
Multi-Objective Linguistic Control of Large Language Models
Dang Nguyen, Jiuhai Chen, Tianyi Zhou
Large language models (LLMs), despite their breakthroughs on many challenging benchmark tasks, lean to generate verbose responses and lack the controllability of output complexity,…
GenQA: Generating Millions of Instructions from a Handful of Prompts
Jiuhai Chen, Rifaa Qadri, Yuxin Wen +4
Most public instruction finetuning datasets are relatively small compared to the closed source datasets used to train industry models. To study questions about finetuning at scale,…
OPTune: Efficient Online Preference Tuning
Lichang Chen, Jiuhai Chen, Chenxi Liu +6
Reinforcement learning with human feedback~(RLHF) is critical for aligning Large Language Models (LLMs) with human preference. Compared to the widely studied offline version of RLH…
Enhancing Visual-Language Modality Alignment in Large Vision Language Models via Self-Improvement
Xiyao Wang, Jiuhai Chen, Zhaoyang Wang +8
Large vision-language models (LVLMs) have achieved impressive results in visual question-answering and reasoning tasks through vision instruction tuning on specific datasets. Howev…
ODIN: Disentangled Reward Mitigates Hacking in RLHF
Lichang Chen, Chen Zhu, Davit Soselia +6
In this work, we study the issue of reward hacking on the response length, a challenge emerging in Reinforcement Learning from Human Feedback (RLHF) on LLMs. A well-formatted, verb…
Can LLMs Speak For Diverse People? Tuning LLMs via Debate to Generate Controllable Controversial Statements
Ming Li, Jiuhai Chen, Lichang Chen +1
Making LLMs speak for different, especially minority groups of people, and generate statements supporting their diverse or even controversial perspectives is critical to creating a…
Selective Reflection-Tuning: Student-Selected Data Recycling for LLM Instruction-Tuning
Ming Li, Lichang Chen, Jiuhai Chen +3
Instruction tuning is critical to large language models (LLMs) for achieving better instruction following and task adaptation capabilities but its success heavily relies on the tra…