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Ethem Yağız Çalık, Talha Rüzgar Akkuş
Advancements in enhancing natural language understanding, conversational coherence, and emotional intelligence in large language models improve user interactions and expand AI applications, while future research will address ethical implications and biases.
Zhiheng Liu, Ka Leong Cheng, Xi Chen +7 authors
MangaNinjia, a diffusion model-based approach, enhances line art colorization with patch shuffling and point-driven control for precise detail and color matching.
Yue Wang, Qiuzhi Liu, Jiahao Xu +11 authors
A new decoding strategy addresses underthinking in large language models by penalizing frequent thought switching, improving accuracy without fine-tuning.
Yadong Li, Jun Liu, Tao Zhang +90 authors
Baichuan-Omni-1.5 is an omni-modal model with end-to-end audio generation, featuring a comprehensive data pipeline, audio-tokenizer, and multi-stage training strategy for superior performance across multimodal tasks.
Stefan Baack, Stella Biderman, Kasia Odrozek +36 authors
Many AI companies are training their large language models (LLMs) on data without the permission of the copyright owners. The permissibility of doing so varies by jurisdiction: in countries like the EU and Japan, this is allowed under certain restrictions, while in the United States, the legal landscape is more ambiguous. Regardless of the legal status, concerns from creative producers have led to several high-profile copyright lawsuits, and the threat of litigation is commonly cited as a reason for the recent trend towards minimizing the information shared about training datasets by both corporate and public interest actors. This trend in limiting data information causes harm by hindering transparency, accountability, and innovation in the broader ecosystem by denying researchers, auditors, and impacted individuals access to the information needed to understand AI models. While this could be mitigated by training language models on open access and public domain data, at the time of writing, there are no such models (trained at a meaningful scale) due to the substantial technical and sociological challenges in assembling the necessary corpus. These challenges include incomplete and unreliable metadata, the cost and complexity of digitizing physical records, and the diverse set of legal and technical skills required to ensure relevance and responsibility in a quickly changing landscape. Building towards a future where AI systems can be trained on openly licensed data that is responsibly curated and governed requires collaboration across legal, technical, and policy domains, along with investments in metadata standards, digitization, and fostering a culture of openness.
Yafu Li, Xuyang Hu, Xiaoye Qu +2 authors
TPO refines LLM outputs in real-time using textual critiques, achieving preference alignment without retraining.
Yubo Wang, Xiang Yue, Wenhu Chen
Critique Fine-Tuning (CFT) improves language model performance by training them to critique noisy responses, showing better reasoning ability than standard Supervised Fine-Tuning (SFT).
Liang Wang, Haonan Chen, Nan Yang +3 authors
CoRAG, a multi-step retrieval and reasoning approach, enhances RAG models by dynamically refining queries and using rejection sampling to improve performance, especially in multi-hop question answering.
Mingjie Pan, Jiyao Zhang, Tianshu Wu +3 authors
A novel object-centric representation bridges the gap between Vision-Language Models' high-level reasoning and the low-level precision needed for robotic manipulation tasks, enabling zero-shot generalization across diverse tasks.
Rui Xie, Yinhong Liu, Penghao Zhou +7 authors
A novel approach using text-to-video models with local information enhancement and dynamic frequency loss improves spatio-temporal quality in real-world video super-resolution.
Alejandro Lozano, Min Woo Sun, James Burgess +13 authors
BIOMEDICA is a scalable framework that creates a large, publicly accessible dataset of image-text pairs from PubMed Central, enabling state-of-the-art performance across various biomedical tasks with efficient CLIP-style models.
Qi Sun, Edoardo Cetin, Yujin Tang
A self-adaptive framework for large language models uses reinforcement learning to dynamically adjust task-specific components during inference, enhancing adaptability and performance with efficiency.
Siyuan Huang, Liliang Chen, Pengfei Zhou +7 authors
EnerVerse enhances robotic manipulation by generating future space using convolutional and bidirectional attention, sparse memory, and generated multi-camera data.
Yichen He, Guanhua Huang, Peiyuan Feng +4 authors
A quality Paper Search agent, PaSa, uses reinforcement learning on synthetic and real academic queries, outperforming Google and GPT-4-based systems.
Qian Chen, Yafeng Chen, Yanni Chen +33 authors
MinMo, a multimodal large language model, integrates speech and text processing to achieve state-of-the-art performance in voice comprehension and generation, while enabling full-duplex conversation and instruction-following capabilities.
Zibo Zhao, Zeqiang Lai, Qingxiang Lin +68 authors
Hunyuan3D 2.0, an advanced 3D synthesis system, uses flow-based diffusion transformers for shape generation and diffusion priors for texture synthesis, outperforming existing models in geometry, condition alignment, and texture quality.
Yuanpeng Tu, Hao Luo, Xi Chen +3 authors
VideoAnydoor is a zero-shot framework for object insertion in videos that preserves detail and controls motion using a text-to-video model, ID extractor, box sequence, pixel warper, and diffusion U-Net.
Shaolei Zhang, Qingkai Fang, Zhe Yang +1 authors
LLaVA-Mini, a unified large multimodal model, significantly reduces computational cost and latency by compressing vision tokens to a single token before feeding them to the LLM backbone, outperforming previous models.
Shanghaoran Quan, Jiaxi Yang, Bowen Yu +14 authors
CodeElo is a standardized code generation benchmark based on CodeForces contests, providing comprehensive evaluation of LLMs' coding abilities with reliable Elo ratings.
Yoav HaCohen, Nisan Chiprut, Benny Brazowski +13 authors
LTX-Video, a transformer-based latent diffusion model, integrates Video-VAE and denoising transformer for efficient high-resolution video generation with temporal consistency.
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