6 citations · 13 across the 7 of their papers we have counts for
9 papers
LiveClin: A Live Clinical Benchmark without Leakage
Xidong Wang, Shuqi Guo, Yue Shen +6
The reliability of medical LLM evaluation is critically undermined by data contamination and knowledge obsolescence, leading to inflated scores on static benchmarks. To address the…
ClinAlign: Scaling Healthcare Alignment from Clinician Preference
Shiwei Lyu, Xidong Wang, Lei Liu +6
Although large language models (LLMs) demonstrate expert-level medical knowledge, aligning their open-ended outputs with fine-grained clinician preferences remains challenging. Exi…
CTRL-RAG: Contrastive Likelihood Reward Based Reinforcement Learning for Context-Faithful RAG Models
Zhehao Tan, Yihan Jiao, Dan Yang +8
With the growing use of Retrieval-Augmented Generation (RAG), training large language models (LLMs) for context-sensitive reasoning and faithfulness is increasingly important. Exis…
Lowest Span Confidence: Zero-Shot Hallucination Detection from a Single LLM Response
Yitong Qiao, Licheng Pan, Yu Mi +6
Hallucinations in Large Language Models (LLMs), i.e., plausible but non-factual generations, pose a significant challenge to reliable deployment in high-stakes environments. Howeve…
GroundingSuite: Measuring Complex Multi-Granular Pixel Grounding
Rui Hu, Lianghui Zhu, Yuxuan Zhang +7
Pixel grounding, encompassing tasks such as Referring Expression Segmentation (RES), has garnered considerable attention due to its immense potential for bridging the gap between v…
One Token to Seg Them All: Language Instructed Reasoning Segmentation in Videos
Zechen Bai, Tong He, Haiyang Mei +6
We introduce VideoLISA, a video-based multimodal large language model designed to tackle the problem of language-instructed reasoning segmentation in videos. Leveraging the reasoni…
EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model
Yuxuan Zhang, Tianheng Cheng, Lianghui Zhu +7
Segment Anything Model (SAM) has attracted widespread attention for its superior interactive segmentation capabilities with visual prompts while lacking further exploration of text…
How Can Large Language Models Understand Spatial-Temporal Data?
Lei Liu, Shuo Yu, Runze Wang +2
While Large Language Models (LLMs) dominate tasks like natural language processing and computer vision, harnessing their power for spatial-temporal forecasting remains challenging.…
Investigate the Essence of Long-Tailed Recognition from a Unified Perspective
Lei Liu, Li Liu
As the data scale grows, deep recognition models often suffer from long-tailed data distributions due to the heavy imbalanced sample number across categories. Indeed, real-world da…