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Alexey Dontsov, Dmitrii Korzh, Alexey Zhavoronkin +6 authors
CLEAR benchmark evaluates multimodal unlearning methods across textual and visual data, highlighting challenges and demonstrating the effectiveness of $\ell_1$ regularization on LoRA weights in mitigating catastrophic forgetting.
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Tianzhu Ye, Li Dong, Yuqing Xia +4 authors
Diff Transformer improves large language models by selectively focusing attention on relevant context and reducing noise, leading to better performance in scaling, long-context modeling, key information retrieval, and in-context learning.
Hongyin Luo, Wei Sun
A new integer addition-based algorithm reduces energy consumption in floating point tensor multiplications with comparable precision across various tasks.
Dongxu Li, Yudong Liu, Haoning Wu +7 authors
Aria is an open multimodal native AI model with best-in-class performance across various tasks, designed with a mixture-of-experts architecture and pre-trained through a four-stage pipeline.
Adam Polyak, Amit Zohar, Andrew Brown +85 authors
Movie Gen, a suite of foundation models, generates high-quality videos with synchronized audio, excelling in various tasks through architectural, training, and technical innovations.
Zesen Cheng, Hang Zhang, Kehan Li +6 authors
A tile-based computation strategy optimizes contrastive loss by partitioning calculations, enabling large batch sizes without significant memory or speed sacrifices.
OpenAI, Aaron Hurst, Adam Lerer +416 authors
GPT-4o is an omnimodal autoregressive model trained to handle text, audio, image, and video inputs, offering high-performance outputs across these modalities, with particular strengths in vision and audio.
Yadong Li, Haoze Sun, Mingan Lin +24 authors
Baichuan-Omni, a 7B open-source Multimodal Large Language Model, excels in processing image, video, audio, and text, showcasing competitive performance across multimodal benchmarks.
Eilam Shapira, Omer Madmon, Itamar Reinman +3 authors
A benchmark for evaluating Large Language Models in strategic, language-based games provides insights into their rationality, mimicry of human behavior, and impact of economic characteristics on performance and outcome.
Chin-Yang Lin, Chung-Ho Wu, Chang-Han Yeh +3 authors
FrugalNeRF addresses few-shot NeRF challenges by using weight-sharing voxels and a cross-scale adaptation scheme, improving quality and reducing training time.
Jinjie Ni, Yifan Song, Deepanway Ghosal +10 authors
MixEval-X is a benchmark designed to optimize and standardize multi-modal evaluations, addressing issues of inconsistency and bias in real-world applications.
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.
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.
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.
Yuzhe Yang, Yifei Zhang, Yan Hu +10 authors
A user-centric framework evaluates LLMs in financial tasks through human feedback and task-specific interactions, achieving high correlation with human preferences.
Maosong Cao, Alexander Lam, Haodong Duan +3 authors
CompassJudger-1 is an open-source, all-in-one judge LLM that evaluates LLMs through unitary scoring, model comparisons, critiques, and various tasks, supported by the JudgerBench benchmark.
Abhishek Thakur
AutoTrain, an open-source tool, streamlines training and fine-tuning of various machine learning models across multiple tasks and modalities using best practices.
Shuai Tan, Biao Gong, Xiang Wang +6 authors
Animate-X, based on latent diffusion models, generates high-quality videos for various character types by enhancing motion representation through the Pose Indicator and a new benchmark, demonstrating superior performance.
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.
Rongyao Fang, Chengqi Duan, Kun Wang +7 authors
PUMA, a unified multimodal large language model, addresses varying granularity demands in visual content generation tasks through the integration of multi-granular visual features.
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.
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.
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.
Shaofei Cai, Zihao Wang, Kewei Lian +4 authors
Visual-temporal context prompting integrates object segmentation and visual observations to enhance VLMs in embodied decision-making, enabling complex spatial tasks.
Jinbin Bai, Tian Ye, Wei Chow +6 authors
Meissonic, a non-autoregressive masked image model, achieves high-quality image generation by incorporating advanced techniques and outperforms existing models like SDXL.
Mingan Lin, Fan Yang, Yanjun Shen +22 authors
Baichuan Alignment provides comprehensive insights into alignment methodologies used in Baichuan models, detailing improvements through Prompt Augmentation System, Supervised Fine-Tuning, and Preference Alignment across various benchmarks.
Ziyue Li, Tianyi Zhou
Mixture-of-Experts (MoE) LLMs can serve as effective embedding models without finetuning, with their routing weights and hidden states complementing each other to enhance performance.
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