32 citations · 50 across the 11 of their papers we have counts for
6 papers · 1 filter
Plum: Prompt Learning using Metaheuristic
Rui Pan, Shuo Xing, Shizhe Diao +6
Since the emergence of large language models, prompt learning has become a popular method for optimizing and customizing these models. Special prompts, such as Chain-of-Thought, ha…
Language-driven Object Fusion into Neural Radiance Fields with Pose-Conditioned Dataset Updates
Ka Chun Shum, Jaeyeon Kim, Binh-Son Hua +2
Neural radiance field is an emerging rendering method that generates high-quality multi-view consistent images from a neural scene representation and volume rendering. Although neu…
Conditional 360-degree Image Synthesis for Immersive Indoor Scene Decoration
Ka Chun Shum, Hong-Wing Pang, Binh-Son Hua +2
In this paper, we address the problem of conditional scene decoration for 360-degree images. Our method takes a 360-degree background photograph of an indoor scene and generates de…
LMFlow: An Extensible Toolkit for Finetuning and Inference of Large Foundation Models
Shizhe Diao, Rui Pan, Hanze Dong +4
Foundation models have demonstrated a great ability to achieve general human-level intelligence far beyond traditional approaches. As the technique keeps attracting attention from…
RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment
Hanze Dong, Wei Xiong, Deepanshu Goyal +7
Generative foundation models are susceptible to implicit biases that can arise from extensive unsupervised training data. Such biases can produce suboptimal samples, skewed outcome…
Automatic Prompt Augmentation and Selection with Chain-of-Thought from Labeled Data
KaShun Shum, Shizhe Diao, Tong Zhang
Chain-of-thought (CoT) advances the reasoning abilities of large language models (LLMs) and achieves superior performance in complex reasoning tasks. However, most CoT studies rely…