124 citations · 142 across the 10 of their papers we have counts for
4 papers · 1 filter
WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct
Haipeng Luo, Qingfeng Sun, Can Xu +8
Large language models (LLMs), such as GPT-4, have shown remarkable performance in natural language processing (NLP) tasks, including challenging mathematical reasoning. However, mo…
WizardCoder: Empowering Code Large Language Models with Evol-Instruct
Ziyang Luo, Can Xu, Pu Zhao +7
Code Large Language Models (Code LLMs), such as StarCoder, have demonstrated exceptional performance in code-related tasks. However, most existing models are solely pre-trained on…
Augmented Large Language Models with Parametric Knowledge Guiding
Ziyang Luo, Can Xu, Pu Zhao +5
Large Language Models (LLMs) have significantly advanced natural language processing (NLP) with their impressive language understanding and generation capabilities. However, their…
Empower Large Language Model to Perform Better on Industrial Domain-Specific Question Answering
Fangkai Yang, Pu Zhao, Zezhong Wang +6
Large Language Model (LLM) has gained popularity and achieved remarkable results in open-domain tasks, but its performance in real industrial domain-specific scenarios is average d…