6 papers
A2RBench: An Automatic Paradigm for Formally Verifiable Abstract Reasoning Benchmark Generation
Qingchuan Ma, Yuexiao Ma, Yongkang Xie +3
Abstract reasoning ability reflects the intelligence and generalization capacity of LLMs to extract and apply abstract rules. However, accurately measuring this ability remains cha…
Video-MME-v2: Towards the Next Stage in Benchmarks for Comprehensive Video Understanding
Chaoyou Fu, Haozhi Yuan, Yuhao Dong +16
With the rapid advancement of video understanding, existing benchmarks are becoming increasingly saturated, exposing a critical discrepancy between inflated leaderboard scores and…
Controlled Automatic Task-Specific Synthetic Data Generation for Hallucination Detection
Yong Xie, Karan Aggarwal, Aitzaz Ahmad +1
We present a novel approach to automatically generate non-trivial task-specific synthetic datasets for hallucination detection. Our approach features a two-step generation-selectio…
Efficient Continual Pre-training for Building Domain Specific Large Language Models
Yong Xie, Karan Aggarwal, Aitzaz Ahmad
Large language models (LLMs) have demonstrated remarkable open-domain capabilities. LLMs tailored for a domain are typically trained entirely on domain corpus to excel at handling…
Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset
Ziye Deng, Ruihan He, Jiaxiang Liu +5
Medical image grounding aims to align natural language phrases with specific regions in medical images, serving as a foundational task for intelligent diagnosis, visual question an…
ReCoM: Realistic Co-Speech Motion Generation with Recurrent Embedded Transformer
Yong Xie, Yunlian Sun, Hongwen Zhang +2
We present ReCoM, an efficient framework for generating high-fidelity and generalizable human body motions synchronized with speech. The core innovation lies in the Recurrent Embed…