7 citations · 11 across the 4 of their papers we have counts for
4 papers
MindStar: Enhancing Math Reasoning in Pre-trained LLMs at Inference Time
Jikun Kang, Xin Zhe Li, Xi Chen +9
Although Large Language Models (LLMs) achieve remarkable performance across various tasks, they often struggle with complex reasoning tasks, such as answering mathematical question…
PanGu-Σ: Towards Trillion Parameter Language Model with Sparse Heterogeneous Computing
Xiaozhe Ren, Pingyi Zhou, Xinfan Meng +14
The scaling of large language models has greatly improved natural language understanding, generation, and reasoning. In this work, we develop a system that trained a trillion-param…
ArCL: Enhancing Contrastive Learning with Augmentation-Robust Representations
Xuyang Zhao, Tianqi Du, Yisen Wang +2
Self-Supervised Learning (SSL) is a paradigm that leverages unlabeled data for model training. Empirical studies show that SSL can achieve promising performance in distribution shi…
When Noisy Labels Meet Long Tail Dilemmas: A Representation Calibration Method
Manyi Zhang, Xuyang Zhao, Jun Yao +2
Real-world large-scale datasets are both noisily labeled and class-imbalanced. The issues seriously hurt the generalization of trained models. It is hence significant to address th…