2 citations · 2 across the 3 of their papers we have counts for
4 papers
Towards Hierarchical Multi-Step Reward Models for Enhanced Reasoning in Large Language Models
Teng Wang, Zhangyi Jiang, Zhenqi He +9
Recent studies show that Large Language Models (LLMs) achieve strong reasoning capabilities through supervised fine-tuning or reinforcement learning. However, a key approach, the P…
BPP-Search: Enhancing Tree of Thought Reasoning for Mathematical Modeling Problem Solving
Teng Wang, Wing-Yin Yu, Zhenqi He +8
LLMs exhibit advanced reasoning capabilities, offering the potential to transform natural language questions into mathematical models. However, existing open-source datasets in ope…
Leveraging Large Language Models for Solving Rare MIP Challenges
Teng Wang, Wing-Yin Yu, Ruifeng She +3
Mixed Integer Programming (MIP) has been extensively applied in areas requiring mathematical solvers to address complex instances within tight time constraints. However, as the pro…
Large Language Models are Good Multi-lingual Learners : When LLMs Meet Cross-lingual Prompts
Teng Wang, Zhenqi He, Wing-Yin Yu +2
With the advent of Large Language Models (LLMs), generating rule-based data for real-world applications has become more accessible. Due to the inherent ambiguity of natural languag…