11 citations · 38 across the 35 of their papers we have counts for
12 papers · 1 filter
MMR1: Enhancing Multimodal Reasoning with Variance-Aware Sampling and Open Resources
Sicong Leng, Jing Wang, Jiaxi Li +12
Large multimodal reasoning models have achieved rapid progress, but their advancement is constrained by two major limitations: the absence of open, large-scale, high-quality long c…
Pruning General Large Language Models into Customized Expert Models
Yirao Zhao, Guizhen Chen, Kenji Kawaguchi +2
Large language models (LLMs) have revolutionized natural language processing, yet their substantial model sizes often require substantial computational resources. To preserve compu…
Analyzing LLMs' Knowledge Boundary Cognition Across Languages Through the Lens of Internal Representations
Chenghao Xiao, Hou Pong Chan, Hao Zhang +4
While understanding the knowledge boundaries of LLMs is crucial to prevent hallucination, research on the knowledge boundaries of LLMs has predominantly focused on English. In this…
Babel: Open Multilingual Large Language Models Serving Over 90% of Global Speakers
Yiran Zhao, Chaoqun Liu, Yue Deng +8
Large language models (LLMs) have revolutionized natural language processing (NLP), yet open-source multilingual LLMs remain scarce, with existing models often limited in language…
LongPO: Long Context Self-Evolution of Large Language Models through Short-to-Long Preference Optimization
Guanzheng Chen, Xin Li, Michael Qizhe Shieh +1
Large Language Models (LLMs) have demonstrated remarkable capabilities through pretraining and alignment. However, superior short-context LLMs may underperform in long-context scen…
SeaExam and SeaBench: Benchmarking LLMs with Local Multilingual Questions in Southeast Asia
Chaoqun Liu, Wenxuan Zhang, Jiahao Ying +3
This study introduces two novel benchmarks, SeaExam and SeaBench, designed to evaluate the capabilities of Large Language Models (LLMs) in Southeast Asian (SEA) application scenari…
FINEREASON: Evaluating and Improving LLMs' Deliberate Reasoning through Reflective Puzzle Solving
Guizhen Chen, Weiwen Xu, Hao Zhang +6
Many challenging reasoning tasks require not just rapid, intuitive responses, but a more deliberate, multi-step approach. Recent progress in large language models (LLMs) highlights…
Finding the Sweet Spot: Preference Data Construction for Scaling Preference Optimization
Yao Xiao, Hai Ye, Linyao Chen +4
Iterative data generation and model retraining are widely used to align large language models (LLMs). It typically involves a policy model to generate on-policy responses and a rew…
VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
Boqiang Zhang, Kehan Li, Zesen Cheng +12
In this paper, we propose VideoLLaMA3, a more advanced multimodal foundation model for image and video understanding. The core design philosophy of VideoLLaMA3 is vision-centric. T…
ECBench: Can Multi-modal Foundation Models Understand the Egocentric World? A Holistic Embodied Cognition Benchmark
Ronghao Dang, Yuqian Yuan, Wenqi Zhang +7
The enhancement of generalization in robots by large vision-language models (LVLMs) is increasingly evident. Therefore, the embodied cognitive abilities of LVLMs based on egocentri…
2.5 Years in Class: A Multimodal Textbook for Vision-Language Pretraining
Wenqi Zhang, Hang Zhang, Xin Li +6
Compared to image-text pair data, interleaved corpora enable Vision-Language Models (VLMs) to understand the world more naturally like humans. However, such existing datasets are c…
VideoRefer Suite: Advancing Spatial-Temporal Object Understanding with Video LLM
Yuqian Yuan, Hang Zhang, Wentong Li +9
Video Large Language Models (Video LLMs) have recently exhibited remarkable capabilities in general video understanding. However, they mainly focus on holistic comprehension and st…