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Peng Xia, Siwei Han, Shi Qiu +9 authors
MMIE is a large-scale benchmark for evaluating interleaved multimodal comprehension and generation in Large Vision-Language Models using diverse data and reliable automated evaluation metrics.
Siyu Zhou, Tianyi Zhou, Yijun Yang +4 authors
A neurosymbolic approach aligns large language models with their environment through rule learning, improving model-based agent performance in open-world tasks.
Jintao Zhang, Jia wei, Pengle Zhang +2 authors
A new quantization method for attention mechanisms, named SageAttention, enhances inference speed and accuracy without significant loss in performance across various models.
Sijie Cheng, Kechen Fang, Yangyang Yu +6 authors
VidEgoThink, a comprehensive benchmark for evaluating egocentric video understanding capabilities, highlights the need for advancements in Multi-modal Large Language Models for effective application in Embodied AI.
Zimu Lu, Aojun Zhou, Ke Wang +5 authors
A novel method for generating mathematical code with reasoning steps enhances mathematical reasoning abilities in large language models using a comprehensive dataset named MathCode-Pile.
Guanting Dong, Xiaoshuai Song, Yutao Zhu +3 authors
VIF-RAG-QA and FollowRAG Benchmark enhance Instruction-Following alignment in RAG systems through automated instruction synthesis, verification, and evaluation.
Yushen Chen, Zhikang Niu, Ziyang Ma +5 authors
F5-TTS, a fully non-autoregressive text-to-speech system, improves E2 TTS by leveraging ConvNeXt and Sway Sampling for better performance and efficiency.
Long Xing, Qidong Huang, Xiaoyi Dong +8 authors
PyramidDrop, a novel strategy for reducing visual token redundancy in large vision-language models, enhances training and inference efficiency with minimal performance loss.
Junyu Luo, Xiao Luo, Xiusi Chen +3 authors
A semi-supervised fine-tuning framework named SemiEvol enhances LLM adaptation using both labeled and unlabeled data, showing improved performance through bi-level knowledge propagation and collaborative learning.
Sijun Tan, Siyuan Zhuang, Kyle Montgomery +5 authors
JudgeBench is a benchmark for evaluating LLM-based judges using objective correctness for challenging tasks like knowledge, reasoning, math, and coding.
Zhuoqun Li, Xuanang Chen, Haiyang Yu +7 authors
StructRAG enhances LLMs by identifying optimal structure types, reconstructing documents into these structures, and inferring answers for knowledge-intensive reasoning tasks, achieving state-of-the-art performance.
Hadas Orgad, Michael Toker, Zorik Gekhman +4 authors
LLMs internally encode detailed truthfulness information about their outputs, which can be used to detect and predict errors but shows variability across datasets and does not always align with the final output.
Chien Van Nguyen, Xuan Shen, Ryan Aponte +25 authors
A survey of small language models covers their architectures, training methods, and model compression techniques, with a taxonomy for optimization, benchmark datasets, evaluation metrics, and open challenges.
Fanqing Meng, Jiaqi Liao, Xinyu Tan +7 authors
PhyGenBench and PhyGenEval assess text-to-video models' understanding of physical commonsense, revealing shortcomings that cannot be fully addressed by scaling models or prompt engineering.
Cheng-De Fan, Chen-Wei Chang, Yi-Ruei Liu +4 authors
SpectroMotion improves upon 3D Gaussian Splatting by integrating physical-based rendering and deformation fields, incorporating a residual correction technique and deformable environment map to accurately reconstruct and synthesize dynamic specular scenes.
Xiang Yue, Yueqi Song, Akari Asai +7 authors
Pangea, a multilingual multimodal LLM, is trained on a diverse 6M instruction dataset across 39 languages and evaluated using PangeaBench, demonstrating superior performance in multilingual and cross-cultural settings.
Hyungjoo Chae, Namyoung Kim, Kai Tzu-iunn Ong +6 authors
A World-model-augmented web agent improves decision-making by simulating action outcomes, overcoming training challenges with free-form natural language descriptions of state differences.
Zecheng Tang, Zechen Sun, Juntao Li +2 authors
LOGO, a preference optimization strategy, enhances LCM generation performance and context window size by efficiently training with limited data.
Fengji Zhang, Linquan Wu, Huiyu Bai +6 authors
HumanEval-V provides a comprehensive benchmark for evaluating LMMs' diagram interpretation and reasoning abilities, particularly in coding contexts, revealing significant areas for improvement.
Aleksei Bochkovskii, Amaël Delaunoy, Hugo Germain +4 authors
Depth Pro is a fast and accurate zero-shot monocular depth estimation model that generates high-resolution depth maps using a multi-scale transformer and a combined real-synthetic training protocol.
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