Towards Comprehensive Preference Data Collection for Reward Modeling

Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models (LLMs) with human preferences, thereby enhancing the quality of responses generated. A critical component of RLHF is the reward model, which is trained on preference data and outputs a scalar reward...

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Veröffentlicht in:	arXiv.org 2024-06
Hauptverfasser:	Hu, Yulan, Li, Qingyang, Ouyang, Sheng, Chen, Ge, Chen, Kaihui, Mei, Lijun, Ye, Xucheng, Zhang, Fuzheng, Liu, Yong
Format:	Artikel
Sprache:	eng
Schlagworte:	Critical components Data collection Large language models Machine learning Preferences
Online-Zugang:	Volltext
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