1 citations · 3 across the 8 of their papers we have counts for
8 papers
Chain-of-Reasoning: Towards Unified Mathematical Reasoning in Large Language Models via a Multi-Paradigm Perspective
Yiyao Yu, Yuxiang Zhang, Dongdong Zhang +9
Large Language Models (LLMs) have made notable progress in mathematical reasoning, yet often rely on single-paradigm reasoning, limiting their effectiveness across diverse tasks. W…
BPO: Towards Balanced Preference Optimization between Knowledge Breadth and Depth in Alignment
Sizhe Wang, Yongqi Tong, Hengyuan Zhang +3
Reinforcement Learning with Human Feedback (RLHF) is the key to the success of large language models (LLMs) in recent years. In this work, we first introduce the concepts of knowle…
ShifCon: Enhancing Non-Dominant Language Capabilities with a Shift-based Multilingual Contrastive Framework
Hengyuan Zhang, Chenming Shang, Sizhe Wang +6
Although fine-tuning Large Language Models (LLMs) with multilingual data can rapidly enhance the multilingual capabilities of LLMs, they still exhibit a performance gap between the…
Incremental Residual Concept Bottleneck Models
Chenming Shang, Shiji Zhou, Hengyuan Zhang +3
Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts and use the concepts to make predic…
Understanding Multimodal Deep Neural Networks: A Concept Selection View
Chenming Shang, Hengyuan Zhang, Hao Wen +1
The multimodal deep neural networks, represented by CLIP, have generated rich downstream applications owing to their excellent performance, thus making understanding the decision-m…
Balancing Speciality and Versatility: A Coarse to Fine Framework for Mitigating Catastrophic Forgetting in Large Language Models
Hengyuan Zhang, Yanru Wu, Dawei Li +4
Aligned Large Language Models (LLMs) showcase remarkable versatility, capable of handling diverse real-world tasks. Meanwhile, aligned LLMs are also expected to exhibit speciality,…