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HalluClean: A Unified Framework to Combat Hallucinations in LLMs
Yaxin Zhao, Yu Zhang
Large language models (LLMs) have achieved impressive performance across a wide range of natural language processing tasks, yet they often produce hallucinated content that undermi…
RPTS: Tree-Structured Reasoning Process Scoring for Faithful Multimodal Evaluation
Haofeng Wang, Yu Zhang
Large Vision-Language Models (LVLMs) excel in multimodal reasoning and have shown impressive performance on various multimodal benchmarks. However, most of these benchmarks evaluat…
Speed Always Wins: A Survey on Efficient Architectures for Large Language Models
Weigao Sun, Jiaxi Hu, Yucheng Zhou +12
Large Language Models (LLMs) have delivered impressive results in language understanding, generation, reasoning, and pushes the ability boundary of multimodal models. Transformer m…
X-Intelligence 3.0: Training and Evaluating Reasoning LLM for Semiconductor Display
Xiaolin Yan, Yangxing Liu, Jiazhang Zheng +53
Large language models (LLMs) have recently achieved significant advances in reasoning and demonstrated their advantages in solving challenging problems. Yet, their effectiveness in…
MOSLIM:Align with diverse preferences in prompts through reward classification
Yu Zhang, Wanli Jiang, Zhengyu Yang
The multi-objective alignment of Large Language Models (LLMs) is essential for ensuring foundational models conform to diverse human preferences. Current research in this field typ…
Multimodal Human-AI Synergy for Medical Imaging Quality Control: A Hybrid Intelligence Framework with Adaptive Dataset Curation and Closed-Loop Evaluation
Zhi Qin, Qianhui Gui, Mouxiao Bian +14
Medical imaging quality control (QC) is essential for accurate diagnosis, yet traditional QC methods remain labor-intensive and subjective. To address this challenge, in this study…