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Hongyu Wang, Shuming Ma, Li Dong +7 authors
BitNet, a 1-bit Transformer architecture, reduces memory and energy consumption while achieving competitive performance in language modeling.
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Akari Asai, Zeqiu Wu, Yizhong Wang +2 authors
Self-Reflective Retrieval-Augmented Generation (Self-RAG) enhances large language models by adaptively retrieving and reflecting on passages, improving factuality and performance across various tasks.
Anton Razzhigaev, Arseniy Shakhmatov, Anastasia Maltseva +7 authors
Kandinsky1, a latent diffusion architecture, achieves high-quality text-to-image generation by integrating image prior models and modified MoVQ autoencoders, outperforming other open-source models.
Mukul Singh, José Cambronero, Sumit Gulwani +3 authors
CodeFusion, a diffusion model for code generation, outperforms auto-regressive models in natural language to code tasks by iteratively refining the entire program.
Junsong Chen, Jincheng Yu, Chongjian Ge +8 authors
PIXART-$\alpha$ is a Transformer-based text-to-image diffusion model that achieves high-quality image synthesis with low training costs using advanced training strategies and efficient architecture.
Chunyi Sun, Junlin Han, Weijian Deng +3 authors
3D-GPT leverages large language models to drive 3D modeling tasks, enhancing scene descriptions and interfacing with 3D software through integrated agents.
Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster +6 authors
Llemma, a large language model pretrained on mathematical data, outperforms existing models and demonstrates tool use and formal theorem proving capabilities.
Seungone Kim, Jamin Shin, Yejin Cho +8 authors
Prometheus, an open-source LLM, matches GPT-4's evaluation capabilities using customized score rubrics and outperforms ChatGPT across various benchmarks.
Eyal Segalis, Dani Valevski, Danny Lumen +2 authors
Relabeling a text-to-image dataset with a specialized automatic captioning model improves image quality and semantic alignment in diffusion models.
Jiatao Gu, Shuangfei Zhai, Yizhe Zhang +2 authors
Matryoshka Diffusion Models use a NestedUNet architecture for joint denoising at multiple resolutions, enabling efficient high-resolution image and video synthesis.
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.
Zhen Xu, Sida Peng, Haotong Lin +5 authors
4K4D, a 4D point cloud representation with a hybrid appearance model and differentiable depth peeling algorithm, achieves high-speed and high-quality dynamic view synthesis at 4K resolution.
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.
Haotian Liu, Chunyuan Li, Yuheng Li +1 authors
Modifications to LLaVA using CLIP-ViT-L-336px and academic VQA data improve multimodal performance, achieving state-of-the-art results with limited training data.
Aaron Gokaslan, A. Feder Cooper, Jasmine Collins +6 authors
A transfer learning technique and efficient training recipe using Creative Commons images enable high-quality text-to-image models with synthetic captions and reduced computational requirements.
Jingkang Yang, Yuhao Dong, Shuai Liu +8 authors
Octopus, a novel vision-language model, integrates multimodal perception and reasoning to perform diverse tasks using GPT-4 and Reinforcement Learning with Environmental Feedback in an experimental environment called OctoVerse.
Omar Khattab, Arnav Singhvi, Paridhi Maheshwari +10 authors
DSPy is a programming model for developing and optimizing language model pipelines by abstracting them as text transformation graphs with parameterized modules that learn through demonstrations.
Aohan Zeng, Mingdao Liu, Rui Lu +4 authors
AgentTuning enhances the agent capabilities of open LLMs through instruction-tuning without compromising their general abilities, demonstrating performance comparable to commercial models on unseen tasks.
Lianghui Zhu, Xinggang Wang, Xinlong Wang
Large Language Models fine-tuned as scalable judges (JudgeLM) achieve state-of-the-art performance in evaluating open-ended benchmarks through a comprehensive dataset and benchmark, enhancing judgment efficiency and accuracy.
Jie Huang, Xinyun Chen, Swaroop Mishra +4 authors
This paper evaluates the intrinsic self-correction capabilities of Large Language Models and identifies limitations in their ability to improve responses without external feedback.
Houwen Peng, Kan Wu, Yixuan Wei +17 authors
A new FP8 automatic mixed-precision framework for training large language models reduces memory usage and increases speed compared to BF16 and Nvidia Transformer Engine.
Pei Zhou, Aman Madaan, Srividya Pranavi Potharaju +9 authors
LLMs perform well in inferring beliefs but struggle translating these into actions; Foresee and Reflect (FaR) improves their action guidance in social scenarios.
Jingxiang Sun, Bo Zhang, Ruizhi Shao +4 authors
DreamCraft3D uses a hierarchical approach with diffusion models and tailored 3D priors to generate high-fidelity and coherent 3D objects from 2D references, addressing geometry consistency and texture fidelity.
Yiheng Xu, Hongjin Su, Chen Xing +13 authors
Lemur and Lemur-Chat are open-source language models combining language and coding capabilities, achieving state-of-the-art performance across various benchmarks and agent tasks.
Dawid Jan Kopiczko, Tijmen Blankevoort, Yuki Markus Asano
Vector-based Random Matrix Adaptation (VeRA) reduces the number of trainable parameters by 10x compared to LoRA while maintaining performance, and is demonstrated on benchmarks like GLUE and E2E, showing its utility in instruction-following.
Weijia Shi, Sewon Min, Maria Lomeli +7 authors
In-Context Pretraining improves large language models by ordering documents to maximize contextual similarity, leading to better performance in complex reasoning tasks.
Yixiao Li, Yifan Yu, Chen Liang +4 authors
LoftQ, a novel quantization framework, improves performance of LLMs by simultaneously quantizing and initializing LoRA fine-tuning, reducing the gap in downstream task performance.
Ke Wang, Houxing Ren, Aojun Zhou +7 authors
A method is presented for fine-tuning language models to solve math problems using code, achieving state-of-the-art results on MATH and GSM8K datasets.
Jianwei Yang, Hao Zhang, Feng Li +3 authors
Set-of-Mark (SoM) enhances visual grounding in large multimodal models using segmented and marked images, demonstrating superior performance on fine-grained vision tasks without full fine-tuning.
Xi Chen, Xiao Wang, Lucas Beyer +16 authors
PaLI-3, a smaller vision language model, outperforms larger models on multimodal tasks, particularly localization and text understanding, using SigLIP pretraining and achieves state-of-the-art results in multilingual cross-modal retrieval.
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