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403 篇论文 · 按点赞排序
Yuhui Xu, Lingxi Xie, Xiaotao Gu +6 authors
QA-LoRA algorithm reduces computational requirements for large language models by quantizing weights during fine-tuning and integrating auxiliary weights post-training.
Guilherme Penedo, Quentin Malartic, Daniel Hesslow +6 authors
Filtered and deduplicated web data alone can produce powerful large language models that outperform those trained on The Pile.
Vladimir Arkhipkin, Andrei Filatov, Viacheslav Vasilev +6 authors
Kandinsky 3.0, a large-scale text-to-image model based on latent diffusion, improves quality and realism through a larger architecture and advanced text understanding.
Xubo Liu, Zhongkai Zhu, Haohe Liu +8 authors
WavJourney leverages Large Language Models to generate structured audio scripts from text, which are then processed by task-specific audio models to create audio content, enabling explainable and human-controlled audio production.
Yiyuan Zhang, Kaixiong Gong, Kaipeng Zhang +4 authors
Meta-Transformer unifies multimodal learning across 12 unpaired modalities using a frozen encoder in a shared token space.
Xiang Li, Yiqun Yao, Xin Jiang +10 authors
A growth strategy reduces the cost of training large language models while achieving comparable performance, with a focus on evaluating IQ and minimizing memorization.
Nelson F. Liu, Kevin Lin, John Hewitt +4 authors
Analysis reveals language models perform better when relevant information is at the ends of longer contexts and suggests new evaluation protocols for long-context models.
Guocheng Qian, Jinjie Mai, Abdullah Hamdi +8 authors
A two-stage neural approach generates high-quality textured 3D meshes from a single image using neural radiance fields, differentiable mesh representations, and combined 2D and 3D diffusion priors.
Shih-Lun Wu, Chris Donahue, Shinji Watanabe +1 authors
Music ControlNet, a diffusion-based model, introduces precise, time-varying control over audio generation, offering enhanced realism and efficiency compared to existing models.
Renat Aksitov, Sobhan Miryoosefi, Zonglin Li +10 authors
A ReAct-style LLM agent with iterative training using reinforcement learning and self-distillation improves complex question-answering performance with fewer parameters.
Yupeng Chang, Xu Wang, Jindong Wang +13 authors
A comprehensive review of evaluation methods for large language models (LLMs) focusing on tasks, methods, and future challenges.
Lianmin Zheng, Wei-Lin Chiang, Ying Sheng +10 authors
Using strong large language models as judges for evaluating other LLM-based chat assistants achieves high agreement with human preferences, offering a scalable and explainable solution compared to traditional benchmarks.
Roee Hendel, Mor Geva, Amir Globerson
In-Context Learning in Large Language Models can be understood as compressing a training set into a task vector that modulates a transformer for output generation.
Yaohui Wang, Xinyuan Chen, Xin Ma +17 authors
LaVie, a text-to-video framework integrating cascaded video latent diffusion models, achieves state-of-the-art video generation by leveraging temporal self-attentions and joint image-video fine-tuning.
Wei-Ge Chen, Irina Spiridonova, Jianwei Yang +2 authors
LLaVA-Interactive is a cost-efficient multimodal dialogue system that integrates visual chat, image segmentation, and generation capabilities to enhance human-AI interaction.
Jason Weston, Sainbayar Sukhbaatar
System 2 Attention in Transformer-based Large Language Models improves factual accuracy and reduces bias by refining input context.
Xian Li, Ping Yu, Chunting Zhou +5 authors
A scalable instruction-following language model is built using auto-labelling and iterative self-augmentation and self-curation, outperforming other LLaMa-based models on Alpaca.
Wangchunshu Zhou, Yuchen Eleanor Jiang, Long Li +14 authors
Agents is an open-source library that facilitates the creation, customization, and deployment of autonomous language agents with features like planning, memory, and tool usage, aiming to make advances in large language models accessible to a broader audience.
Bo Shen, Jiaxin Zhang, Taihong Chen +9 authors
PanGu-Coder2, a model fine-tuned with RRTF framework, demonstrates superior code generation performance, outperforming existing Code LLMs on multiple benchmarks.
Timo I. Denk, Yu Takagi, Takuya Matsuyama +4 authors
A method reconstructs music from fMRI data using the MusicLM model, matching semantic properties of original stimuli and identifying brain regions involved in processing music descriptions.
Lukas Blecher, Guillem Cucurull, Thomas Scialom +1 authors
Nougat, a Visual Transformer model, performs OCR on scientific documents converting them to markup language, enhancing digital accessibility.
Róbert Csordás, Piotr Piękos, Kazuki Irie +1 authors
SwitchHead reduces memory and compute for Transformers by using Mixture-of-Experts layers, matching performance with significant speedup.
Minghua Liu, Chao Xu, Haian Jin +4 authors
A novel feed-forward method generates high-quality 360-degree 3D meshes from single images using a view-conditioned 2D diffusion model and an SDF-based neural surface reconstruction module.
Chi Han, Qifan Wang, Wenhan Xiong +3 authors
LM-Infinite addresses length generalization in Transformer-based LLMs using a simple $\Lambda$-shaped attention mask, achieving fluency and quality up to 32k tokens without retraining.
Chunyuan Li, Zhe Gan, Zhengyuan Yang +4 authors
The survey covers the development and advancements in multimodal foundation models that integrate vision and language, transitioning from specialized models to versatile assistants through various training methods and unified architectures.
Peng Li, Yeye He, Dror Yashar +6 authors
A new table-tuning paradigm improves language models' understanding and generalization in table-related tasks by fine-tuning them with synthesized table data, resulting in enhanced performance.
Luka Ribar, Ivan Chelombiev, Luke Hudlass-Galley +3 authors
SparQ Attention reduces memory bandwidth requirements in LLM attention blocks, enhancing inference throughput without accuracy loss.
Yining Hong, Haoyu Zhen, Peihao Chen +4 authors
A new family of 3D-LLMs is introduced to perform 3D-related tasks by leveraging 3D point clouds and features, outperforming state-of-the-art baselines in tasks such as 3D question answering and captioning.
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.
Shehzaad Dhuliawala, Mojtaba Komeili, Jing Xu +4 authors
The Chain-of-Verification method reduces hallucinations in language models by having them draft, plan verification questions, answer independently, and then generate a final verified response.
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