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Ariel N. Lee, Cole J. Hunter, Nataniel Ruiz
A fine-tuned and merged family of large language models named Platypus, using LoRA modules and a curated dataset, achieves top performance on the Open LLM Leaderboard with reduced data and compute.
Huichao Zhang, Bowen Chen, Hao Yang +7 authors
AvatarVerse generates high-fidelity 3D avatars from text descriptions and pose guidance using a 2D diffusion model and progressive high-resolution synthesis.
Elad Richardson, Kfir Goldberg, Yuval Alaluf +1 authors
Creative text-to-image generation leverages Diffusion Prior models to produce unique images by optimizing with adaptive constraints and creating hybrid concepts.
Wenxuan Zhou, Sheng Zhang, Yu Gu +2 authors
Targeted distillation using mission-focused instruction tuning distills ChatGPT into the smaller UniversalNER model, achieving higher NER accuracy across diverse domains than both general and state-of-the-art multi-task tuned models.
Ning Miao, Yee Whye Teh, Tom Rainforth
A zero-shot verification scheme improves LLMs' question-answering performance by identifying and correcting errors in step-by-step reasoning.
Jiaao Chen, Xiaoman Pan, Dian Yu +4 authors
A novel prompting strategy called skills-in-context (SKiC) enhances compositional generalization in large language models by enabling them to solve unseen complex problems through the synergistic activation and combination of internal skills.
Shengming Yin, Chenfei Wu, Jian Liang +4 authors
DragNUWA is a diffusion-based video generation model that integrates text, image, and trajectory controls for fine-grained and open-domain video creation.
Jerry Wei, Da Huang, Yifeng Lu +2 authors
Studies reveal that language models exhibit sycophancy, altering responses to align with user views, and a synthetic-data intervention can mitigate this behavior.
Zheng Yuan, Hongyi Yuan, Chengpeng Li +3 authors
Rejection sampling fine-tuning enhances mathematical reasoning in large language models by generating auxiliary datasets of correct reasoning paths, leading to higher performance and better scalability compared to supervised fine-tuning.
Yu-Ting Lan, Kan Ren, Yansen Wang +4 authors
A pipeline named NeuroImagen uses EEG signals and a latent diffusion model to reconstruct high-resolution visual images, demonstrating superior performance in image reconstruction.
Joan Puigcerver, Carlos Riquelme, Basil Mustafa +1 authors
Soft MoE, a differentiable sparse Transformer, stabilizes training, reduces inference cost, and outperforms traditional Transformers and MoE variants in visual recognition.
Andrea Gesmundo, Kaitlin Maile
The research introduces methods to incrementally enlarge transformer-based neural networks while preserving their functionality, enabling efficient training of larger models.
Weiran Yao, Shelby Heinecke, Juan Carlos Niebles +12 authors
A framework using policy gradient optimization to enhance large language agents by learning from environment feedback, improving their performance over time.
Wenqi Shao, Mengzhao Chen, Zhaoyang Zhang +7 authors
OmniQuant improves LLM deployment by optimizing quantization parameters through Learnable Weight Clipping and Learnable Equivalent Transformation, enhancing performance across various quantization settings.
Zhiwei Liu, Weiran Yao, Jianguo Zhang +12 authors
A comprehensive comparison and new orchestration strategy for LLM-augmented Autonomous Agents (LAAs) demonstrate their effectiveness in decision-making and multi-step reasoning tasks.
Thomas McGrath, Matthew Rahtz, Janos Kramar +2 authors
Analysis reveals adaptive computation and downregulation mechanisms in language models, showing loose coupling among layers even without dropout.
Jie Huang, Wei Ping, Peng Xu +3 authors
RAVEN enhances in-context learning in retrieval-augmented encoder-decoder models by combining masked and prefix language modeling, achieving superior performance with fewer parameters.
Nan Ding, Tomer Levinboim, Jialin Wu +2 authors
Theoretical analysis shows that prefix language models outperform causal language models in in-context learning by converging to optimal solutions, while causal models exhibit dynamics similar to online gradient descent.
Weihao Yu, Zhengyuan Yang, Linjie Li +5 authors
MM-Vet evaluates large multimodal models across complicated tasks using defined vision-language capabilities and a unified LLM-based evaluator.
Ke Chen, Yusong Wu, Haohe Liu +3 authors
A text-to-music model named MusicLDM, based on Stable Diffusion and AudioLDM, overcomes limitations in music data and plagiarism with beat tracking and mixup strategies, enhancing music quality, novelty, and text correspondence.
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