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Sean O'Brien, Mike Lewis
Contrastive Decoding, a simple and training-free text generation method, outperforms other decoding techniques on various reasoning tasks and logical benchmarks by improving perceived quality and reducing reasoning errors.
Jinze Bai, Shuai Bai, Yunfei Chu +45 authors
Qwen, a series of large language models, including chat and specialized coding and mathematics variants, exhibit superior performance across various tasks and outperform open-source models.
Kumar Tanmay, Aditi Khandelwal, Utkarsh Agarwal +1 authors
A new evaluation framework connects human psychology and AI to assess the moral reasoning and development stages of large language models using a Psychometric Assessment Tool.
Yung-Sung Chuang, Yujia Xie, Hongyin Luo +3 authors
A decoding strategy that contrasts logits from different transformer layers reduces hallucinations in large language models without external knowledge or fine-tuning.
Han Lin, Abhay Zala, Jaemin Cho +1 authors
VideoDirectorGPT combines LLMs for video content planning with a layout-controlled video generator, enabling temporally consistent multi-scene video generation from text prompts.
Qihang Fan, Huaibo Huang, Mingrui Chen +2 authors
The proposed RMT model, combining RetNet and Transformer architectures, introduces explicit spatial distance priors and coordinate-wise decomposition to achieve exceptional performance in computer vision tasks.
Xingchao Liu, Xiwen Zhang, Jianzhu Ma +2 authors
Rectified Flow is used to develop an ultra-fast one-step text-to-image generator named InstaFlow, achieving high image quality with significantly reduced inference time compared to existing methods.
Griffin Adams, Alexander Fabbri, Faisal Ladhak +2 authors
CoD prompt in GPT-4 improves summary density, informativeness, and human preference over vanilla prompts, with tradeoffs between readability and detail.
Telmo Pessoa Pires, António V. Lopes, Yannick Assogba +1 authors
The study shows that the Feed Forward Network in Transformers is highly redundant and its reduction or sharing within the model can lead to improved accuracy and latency without significant loss of performance.
Zilong Chen, Feng Wang, Huaping Liu
Gaussian Splatting based text-to-3D generation improves geometry accuracy and fidelity through progressive optimization, including geometry optimization and appearance refinement.
Xiaoliang Dai, Ji Hou, Chih-Yao Ma +23 authors
Quality-tuning enhances the aesthetic appeal of text-to-image models through supervised fine-tuning with a small set of high-quality images.
Haoran Xu, Young Jin Kim, Amr Sharaf +1 authors
A novel fine-tuning approach for moderate-sized language models significantly improves translation performance with less parallel data compared to traditional methods.
Zhiqing Sun, Sheng Shen, Shengcao Cao +9 authors
The paper presents Factually Augmented RLHF to address multimodal misalignment in large multimodal models, significantly improving hallucination reduction and overall performance on vision-language tasks compared to existing methods.
Wenhan Xiong, Jingyu Liu, Igor Molybog +18 authors
Long-context LLMs achieve significant advancements in handling extended contexts through continual pretraining and efficient instruction tuning, surpassing existing models on various benchmarks.
Aliasghar Khani, Saeid Asgari Taghanaki, Aditya Sanghi +2 authors
SLiMe segments images at desired granularity using Stable Diffusion with minimal annotations and outperforms existing one-shot and few-shot segmentation methods.
Haohe Liu, Ke Chen, Qiao Tian +2 authors
A diffusion-based generative model, AudioSR, achieves robust audio super-resolution across various audio types and bandwidths, enhancing generation quality for different audio models.
Ho Kei Cheng, Seoung Wug Oh, Brian Price +2 authors
DEVA, a decoupled approach using image-level segmentation and bi-directional temporal propagation, achieves favorable results in various data-scarce video segmentation tasks with reduced annotation and training costs.
Dave Van Veen, Cara Van Uden, Louis Blankemeier +12 authors
Domain-adapted large language models outperform human experts in various clinical text summarization tasks, improving completeness, correctness, and aligning with physician preferences.
Junha Hyung, Jaeyo Shin, Jaegul Choo
MagiCapture personalizes text-to-image models by integrating subject and style concepts using limited references and an Attention Refocusing loss, enhancing realism and quality in portrait image generation.
Shangchen Zhou, Chongyi Li, Kelvin C. K. Chan +1 authors
ProPainter is a framework that improves video inpainting by combining dual-domain propagation and a mask-guided sparse video Transformer, outperforming existing methods in PSNR while maintaining efficiency.
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