NettetWe collect a large, high-quality dataset of human comparisons between summaries, train a model to predict the human-preferred summary, and use that model as a reward function to fine-tune a summarization policy using reinforcement learning. We apply our method to a version of the TL;DR dataset of Reddit posts and find that our models ... NettetSummary and Contributions: This paper explores using RL (PPO) to learn an abstractive summarization model from human feedback. Humans are presented with ground …

[大语言模型之RLHF]Learning to summarize from human …

Nettet10. apr. 2024 · Learning to summarize from human feedback导读（1）. （2）我们首先收集成对摘要之间的人类偏好数据集，然后通过监督学习训练奖励模型 (RM)来预测人类偏好的摘要。. 最后，我们通过强化学习 (RL)来训练策略，以最大化RM给出的分数;该策略在每个“时间步骤”生成一个 ... NettetWe conduct extensive analyses to understand our human feedback dataset and fine-tuned models. We establish that our reward model generalizes to new datasets, and that optimizing our reward model results in better summaries than optimizing ROUGE according to humans. We hope the evidence from our paper motivates machine … phipps road duncan

Learning to summarize with human feedback

Nettet5 timer siden · Surveillance cameras have recently been utilized to provide physical security services globally in diverse private and public spaces. The number of cameras has been increasing rapidly due to the need for monitoring and recording abnormal events. This process can be difficult and time-consuming when detecting anomalies using … Nettet4. mar. 2024 · In this paper, we show an avenue for aligning language models with user intent on a wide range of tasks by fine-tuning with human feedback. Starting with a set of labeler-written prompts and prompts submitted through the OpenAI API, we collect a dataset of labeler demonstrations of the desired model behavior, which we use to fine … Nettet10. apr. 2024 · Learning to summarize from human feedback导读（1）. （2）我们首先收集成对摘要之间的人类偏好数据集，然后通过监督学习训练奖励模型 (RM)来预测人 … tsp in spanish