Explore · 每周精选
发现最受关注的研究论文,追踪研究趋势,订阅感兴趣的期刊与关键词。
598 篇论文 · 按点赞排序
DeepSeek-AI, Qihao Zhu, Daya Guo +37 authors
DeepSeek-Coder-V2, a Mixture-of-Experts language model, excels in code-specific tasks by enhancing coding and mathematical reasoning capabilities while expanding language support and context length.
Sushant Gautam, Andrea Storås, Cise Midoglu +4 authors
Kvasir-VQA is a dataset with question-and-answer annotations for GI diagnostics, supporting image captioning, VQA, synthetic image generation, object detection, and classification.
David Junhao Zhang, Roni Paiss, Shiran Zada +7 authors
ReCapture generates new videos with different camera trajectories from a user-provided video using multiview diffusion models and masked video fine-tuning.
Pat Verga, Sebastian Hofstatter, Sophia Althammer +6 authors
Evaluating Large Language Models using a Panel of smaller evaluators outperforms single large evaluators with reduced bias and cost.
Peize Sun, Yi Jiang, Shoufa Chen +4 authors
LlamaGen applies the next-token prediction paradigm from large language models to image generation, achieving state-of-the-art performance with various model sizes and conditions.
Patrick Esser, Sumith Kulal, Andreas Blattmann +14 authors
Improved noise sampling techniques and a transformer-based architecture enhance rectified flow models for high-resolution text-to-image synthesis, outperforming existing methods.
Hongyu Wang, Shuming Ma, Furu Wei
BitNet a4.8 enhances the efficiency of large language models through 4-bit quantization and sparsification, achieving equivalent performance to BitNet b1.58 with reduced inference costs.
Denis Bobkov, Vadim Titov, Aibek Alanov +1 authors
StyleFeatureEditor combines W-space and F-space for effective image attribute manipulation in StyleGAN, balancing reconstruction quality and detailed editing.
Shuangrui Ding, Rui Qian, Xiaoyi Dong +6 authors
SAM2Long enhances SAM 2's video segmentation by using a memory module and constrained tree search to improve accuracy in complex, long-term videos.
Anian Ruoss, Grégoire Delétang, Sourabh Medapati +5 authors
A large-scale transformer model trained on a vast dataset of chess games outperforms traditional chess engines and other state-of-the-art models without domain-specific tweaks.
Antoine Grosnit, Alexandre Maraval, James Doran +15 authors
Agent K v1.0 automates data science tasks across various domains using structured reasoning and Bayesian optimization, achieving performance comparable to Expert-level Kaggle users.
Renjie Pi, Jianshu Zhang, Tianyang Han +3 authors
A new framework called Personalized Visual Instruction Tuning (PVIT) enhances multimodal large language models to recognize and engage with specific individuals in images, utilizing a curated dataset and benchmarks for evaluation.
Yiheng Xu, Zekun Wang, Junli Wang +6 authors
Aguvis is a unified, vision-based framework for autonomous GUI agents that integrates language grounding and explicit planning, enabling cross-platform generalization and outperforming existing methods.
Zecheng He, Bo Sun, Felix Juefei-Xu +14 authors
Imagine yourself is a tuning-free diffusion model for personalized image generation that enhances identity preservation, text alignment, and visual quality through synthetic data generation, parallel attention architecture, and multi-stage fine-tuning.
Pravesh Agrawal, Szymon Antoniak, Emma Bou Hanna +34 authors
Pixtral-12B, a 12-billion-parameter multimodal language model, excels in both natural language and image understanding, surpassing larger models and introducing an open-source benchmark for evaluation.
Yushun Zhang, Congliang Chen, Ziniu Li +5 authors
Adam-mini reduces memory usage by optimizing learning rate allocation in parameter blocks, achieving comparable or better performance than AdamW with increased throughput.
Zixiang Chen, Yihe Deng, Huizhuo Yuan +2 authors
A novel self-play fine-tuning method (SPIN) enhances Large Language Models by generating its own training data and refining its policy, achieving performance on par with models trained using direct preference optimization and additional human data.
Yushi Bai, Jiajie Zhang, Xin Lv +6 authors
AgentWrite enables long context LLMs to generate outputs exceeding 20,000 words by decomposing tasks into subtasks, using a new dataset to train models with extended output capabilities.
Lichao Sun, Yue Huang, Haoran Wang +64 authors
This study assesses the trustworthiness of large language models across various dimensions, including truthfulness, safety, fairness, robustness, privacy, and machine ethics, finding a positive correlation with utility and highlighting differences between proprietary and open-source models.
Ilya Gusev
A benchmark evaluates language models' role-playing abilities using automated components to simulate and judge dialogues.
Sander Schulhoff, Michael Ilie, Nishant Balepur +28 authors
A taxonomy and meta-analysis are provided for prompting techniques and vocabulary in GenAI systems, with a focus on natural language prefix-prompting.
Leon Voukoutis, Dimitris Roussis, Georgios Paraskevopoulos +6 authors
Developers created Meltemi 7B, a 7 billion parameter open-source large language model for Greek, trained on a 40 billion token corpus, and enhanced with instruction-tuning for chat applications.
Tri Dao, Albert Gu
A new framework connects state-space models and transformers, leading to a faster architecture for language modeling.
Nathan Lambert, Jacob Morrison, Valentina Pyatkin +20 authors
T\"ULU 3, an open-source family of post-trained language models, introduces transparent training data, recipes, and advanced techniques to match or surpass proprietary models in performance.
Yiwen Hu, Huatong Song, Jia Deng +8 authors
YuLan-Mini, a 2.42B parameter base model, achieves top-tier performance with efficient pre-training techniques, including a data pipeline with cleaning and scheduling, robust optimization, and annealing with targeted data selection.
Yoad Tewel, Rinon Gal, Dvir Samuel Yuval Atzmon +2 authors
Add-it extends diffusion models with a weighted extended-attention mechanism to seamlessly integrate objects into images based on text instructions, achieving state-of-the-art results on real and generated image insertion benchmarks.
Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach +3 authors
LLM2Vec transforms decoder-only LLMs into effective text encoders using bidirectional attention, masked next token prediction, and unsupervised contrastive learning, achieving state-of-the-art performance on text embedding tasks.
Charlie Snell, Jaehoon Lee, Kelvin Xu +1 authors
Research on scaling test-time computation in LLMs finds that compute-optimal strategies can enhance performance and efficiency beyond larger base models.
Yoad Tewel, Omri Kaduri, Rinon Gal +4 authors
ConsiStory achieves state-of-the-art text-to-image subject consistency without fine-tuning by using shared internal activations, attention blocks, and feature injection.
Mehmet Saygin Seyfioglu, Karim Bouyarmane, Suren Kumar +2 authors
Diffuse to Choose is a diffusion-based inpainting model that balances real-time performance with high-fidelity detail preservation and semantic accuracy.
北京市昌平区探索星信息技术及软件开发工作室
京ICP备2026059466号