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Byung-Kwan Lee, Chae Won Kim, Beomchan Park +1 authors
Meteor, an efficient large language and vision model, enhances understanding and answering capabilities by embedding multifaceted rationales using the Mamba architecture, leading to improved vision language performance without increasing model size or using additional vision encoders.
Sean McLeish, Arpit Bansal, Alex Stein +8 authors
Transformers achieve state-of-the-art performance on large arithmetic tasks and other reasoning tasks by addressing positional tracking with embeddings and integrating architectural modifications.
Bohao Peng, Jian Wang, Yuechen Zhang +3 authors
ControlNeXt is a computationally efficient method for controllable image and video generation with a streamlined architecture and minimal parameters, using Cross Normalization for fast training convergence.
Yiruo Cheng, Kelong Mao, Ziliang Zhao +6 authors
CORAL is a large-scale benchmark for evaluating Retrieval-Augmented Generation (RAG) systems in multi-turn conversational settings, addressing challenges like open-domain coverage, knowledge intensity, free-form responses, and topic shifts.
Chufan Shi, Cheng Yang, Yaxin Liu +11 authors
ChartMimic is a new benchmark assessing large multimodal models' visually-grounded code generation for chart rendering, evaluating multi-level cognitive capabilities with diverse chart types and metrics.
Baixiang Huang, Canyu Chen, Xiongxiao Xu +2 authors
HalluEditBench benchmarks knowledge editing methods in correcting hallucinations in Large Language Models (LLMs) using a comprehensive dataset and evaluation framework.
Kepan Nan, Rui Xie, Penghao Zhou +6 authors
The paper introduces OpenVid-1M and OpenVidHD-0.4M datasets, along with MVDiT, a Multi-modal Video Diffusion Transformer, to enhance text-to-video generation by leveraging high-quality data and comprehensive text information.
Xiaoyi Dong, Pan Zhang, Yuhang Zang +20 authors
InternLM-XComposer2, a vision-language model, excels in free-form text-image creation and understanding using Partial LoRA, surpassing existing models and matching GPT-4V and Gemini Pro in multimodal benchmarks.
Jihwan Kim, Junoh Kang, Jinyoung Choi +1 authors
FIFO-Diffusion generates long text-conditional videos through iterative denoising with latent partitioning and lookahead strategies to minimize the training-inference gap.
Yubo Wang, Xueguang Ma, Ge Zhang +14 authors
MMLU-Pro extends the MMLU benchmark with more challenging reasoning questions, eliminates trivial ones, and demonstrates better stability and discriminative power for language models.
Hengxing Cai, Xiaochen Cai, Shuwen Yang +14 authors
Uni-SMART, a multimodal analysis model, outperforms LLMs in understanding scientific literature with diverse content and has applications in patent infringement and chart analysis.
Jiarui Zhang, Ollie Liu, Tianyu Yu +2 authors
A benchmark called Geoperception is introduced to evaluate MLLMs' geometric description accuracy, leading to the development of Euclid, a model optimized for low-level geometric perception using synthetic data and a data curriculum.
Bin Lin, Zhenyu Tang, Yang Ye +6 authors
MoE-LLaVA, a sparse Large Vision-Language Model architecture, improves performance and reduces hallucinations with fewer active parameters compared to larger models.
Chengyue Wu, Yukang Gan, Yixiao Ge +5 authors
A new post-pretraining method using expanded Transformer blocks for Large Language Models improves knowledge without catastrophic forgetting, yielding LLaMA Pro-8.3B that excels in general tasks, programming, and mathematics.
Jun Zhao, Zhihao Zhang, Qi Zhang +2 authors
A comprehensive study on transferring English-dominant LLMs to non-English languages using LLaMA demonstrates that comparable performance can be achieved with minimal pretraining data across various language capabilities and response quality metrics.
Tao Zhang, Xiangtai Li, Hao Fei +5 authors
OMG-LLaVA integrates pixel-level vision understanding and reasoning capabilities through a universal segmentation method combined with LLM, achieving high performance across multiple benchmarks.
Ye Bai, Haonan Chen, Jitong Chen +35 authors
Seed-Music generates high-quality music with style control using a unified framework combining auto-regressive language modeling and diffusion approaches, supporting vocal music generation and post-production editing.
Xidong Wang, Dingjie Song, Shunian Chen +2 authors
LongLLaVA, a hybrid MLLM combining Mamba and Transformer blocks, enhances video and image understanding with improved efficiency and throughput.
Junyan Ye, Baichuan Zhou, Zilong Huang +12 authors
LOKI is a benchmark for evaluating the synthetic data detection capabilities of large multimodal models across various modalities.
Zhengfeng Lai, Vasileios Saveris, Chen Chen +9 authors
A study examines the use of synthetic and AltText captions in multimodal models, finding that a hybrid approach using both types improves alignment and performance across various models.
Haotian Zhang, Mingfei Gao, Zhe Gan +20 authors
MM1.5 enhances multimodal language models with a data-centric approach, supporting diverse tasks like text-rich image understanding and video comprehension through careful data curation and training strategies.
Ming Zhong, Aston Zhang, Xuewei Wang +14 authors
LLMs show suboptimal performance in cross-capability tasks, constrained by their weakest individual capabilities, as demonstrated by the CrossEval benchmark.
Niklas Muennighoff, Hongjin Su, Liang Wang +5 authors
GrIT allows large language models to excel at both generative and embedding tasks through task instruction, leading to new state-of-the-art performance without sacrificing efficiency.
Pujiang He, Shan Zhou, Wenhuan Huang +7 authors
The paper presents a performance optimization solution for deploying large language models on CPUs by reducing KV cache size and implementing distributed inference, open-sourced for use.
Renrui Zhang, Dongzhi Jiang, Yichi Zhang +8 authors
MathVerse is a comprehensive visual math benchmark with CoT evaluation to assess MLLMs' true understanding and reasoning quality from visual diagrams.
Shantanu Acharya, Fei Jia, Boris Ginsburg
Star Attention improves inference efficiency of large language models on long sequences by using block-sparse approximation, reducing memory and time without significant accuracy loss.
Di Zhang, Jianbo Wu, Jingdi Lei +9 authors
LLaMA-Berry enhances LLMs for mathematical reasoning by combining Monte Carlo Tree Search with Self-Refine and leveraging a Pairwise Preference Reward Model to optimize search efficiency and problem-solving capability.
Zhifei Xie, Changqiao Wu
Mini-Omni is an end-to-end audio-based conversational model enabling real-time speech interaction and efficient speech generation.
Ting-Hsuan Chen, Jiewen Chan, Hau-Shiang Shiu +3 authors
A video editing framework combining deformation fields and diffusion priors produces high-quality canonical images for diverse video editing tasks with accelerated training using low-rank adaptation.
Shangda Wu, Xu Tan, Zili Wang +3 authors
bGPT, a model using next byte prediction, simulates various digital processes with high accuracy, matching specialized models in performance and offering insights into algorithm and hardware behavior.
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