1 citations · 1 across the 4 of their papers we have counts for
5 papers
What Matters in LLM-generated Data: Diversity and Its Effect on Model Fine-Tuning
Yuchang Zhu, Huazhen Zhong, Qunshu Lin +6
With the remarkable generative capabilities of large language models (LLMs), using LLM-generated data to train downstream models has emerged as a promising approach to mitigate dat…
FormalMATH: Benchmarking Formal Mathematical Reasoning of Large Language Models
Zhouliang Yu, Ruotian Peng, Keyi Ding +10
Formal mathematical reasoning remains a critical challenge for artificial intelligence, hindered by limitations of existing benchmarks in scope and scale. To address this, we prese…
Objaverse++: Curated 3D Object Dataset with Quality Annotations
Chendi Lin, Heshan Liu, Qunshu Lin +6
This paper presents Objaverse++, a curated subset of Objaverse enhanced with detailed attribute annotations by human experts. Recent advances in 3D content generation have been dri…
MetaOcc: Spatio-Temporal Fusion of Surround-View 4D Radar and Camera for 3D Occupancy Prediction with Dual Training Strategies
Long Yang, Lianqing Zheng, Wenjin Ai +8
Robust 3D occupancy prediction is essential for autonomous driving, particularly under adverse weather conditions where traditional vision-only systems struggle. While the fusion o…
AutoKaggle: A Multi-Agent Framework for Autonomous Data Science Competitions
Ziming Li, Qianbo Zang, David Ma +11
Data science tasks involving tabular data present complex challenges that require sophisticated problem-solving approaches. We propose AutoKaggle, a powerful and user-centric frame…