109 citations · 232 across the 15 of their papers we have counts for
10 papers · 1 filter
WarriorCoder: Learning from Expert Battles to Augment Code Large Language Models
Huawen Feng, Pu Zhao, Qingfeng Sun +8
Despite recent progress achieved by code large language models (LLMs), their remarkable abilities are largely dependent on fine-tuning on the high-quality data, posing challenges f…
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task Generation
Mengkang Hu, Pu Zhao, Can Xu +5
Large Language Model-based agents have garnered significant attention and are becoming increasingly popular. Furthermore, planning ability is a crucial component of an LLM-based ag…
Arena Learning: Build Data Flywheel for LLMs Post-training via Simulated Chatbot Arena
Haipeng Luo, Qingfeng Sun, Can Xu +6
Assessing the effectiveness of large language models (LLMs) presents substantial challenges. The method of conducting human-annotated battles in an online Chatbot Arena is a highly…
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…
WizardLM: Empowering large pre-trained language models to follow complex instructions
Can Xu, Qingfeng Sun, Kai Zheng +6
Training large language models (LLMs) with open-domain instruction following data brings colossal success. However, manually creating such instruction data is very time-consuming a…
MMDialog: A Large-scale Multi-turn Dialogue Dataset Towards Multi-modal Open-domain Conversation
Jiazhan Feng, Qingfeng Sun, Can Xu +5
Responding with multi-modal content has been recognized as an essential capability for an intelligent conversational agent. In this paper, we introduce the MMDialog dataset to bett…
Stylized Knowledge-Grounded Dialogue Generation via Disentangled Template Rewriting
Qingfeng Sun, Can Xu, Huang Hu +6
Current Knowledge-Grounded Dialogue Generation (KDG) models specialize in producing rational and factual responses. However, to establish long-term relationships with users, the KD…
PromDA: Prompt-based Data Augmentation for Low-Resource NLU Tasks
Yufei Wang, Can Xu, Qingfeng Sun +4
This paper focuses on the Data Augmentation for low-resource Natural Language Understanding (NLU) tasks. We propose Prompt-based D}ata Augmentation model (PromDA) which only trains…
Multimodal Dialogue Response Generation
Qingfeng Sun, Yujing Wang, Can Xu +7
Responsing with image has been recognized as an important capability for an intelligent conversational agent. Yet existing works only focus on exploring the multimodal dialogue mod…