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cs.AI2024
Towards Comprehensive Preference Data Collection for Reward Modeling
Yulan Hu, Qingyang Li, Sheng Ouyang +6
Reinforcement Learning from Human Feedback (RLHF) facilitates the alignment of large language models (LLMs) with human preferences, thereby enhancing the quality of responses gener…
cs.AI2024
Chain-of-Specificity: An Iteratively Refining Method for Eliciting Knowledge from Large Language Models
Kaiwen Wei, Jingyuan Zhang, Hongzhi Zhang +4
Large Language Models (LLMs) exhibit remarkable generative capabilities, enabling the generation of valuable information. Despite these advancements, previous research found that L…