4.1. Summarizing Reddit posts from human feedback Policies trained with human feedback are preferred to much larger supervised policies:基于人类反馈进行微调的模型,1.3B的效果即可超越12.9B的效果,而且随着模型规模的增加,效果会进一步提升 Controlling for summary length:由于我们的模型倾向于产生较长的摘要,这...
internet, some fine-tuned via supervised learning to predict TL;DRs, and some fine-tuned using human feedback.DTo evaluate each model, we had it summarize posts from the validation set and asked humans to compare their summaries to the human-written TL;DR. The results are shown inFigure 1...
Learning to summarize from human feedbackarxiv.org/abs/2009.01325 这篇工作是OpenAI早于ChatGPT两年发表在NeurIPS 2020,在这篇文章中ChatGPT的框架雏形已经显现。文章专注于英文摘要任务,所用的方法和ChatGPT基本一致,包括了ChatGPT的核心方法,RLHF(Reinforcement Learning from Human Feedback)。 问题 随着语言...
(1)生成摘要等模型,虽然有评估方法,但是人类总结的质量依旧难以相比 总结: (1)在各种NLP任务中,大规模语言模型的预训练以及取得了很高的性能 (2) 当把大模型用于下游任务的时候,通常需要监督数据进行微调,数据一般来源于人类总结的证据,来最大化似然函数 (3)这些方法提高了性能,但是呢存在无法对其的问题,微调最大...
(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”生成一个文本令牌,并使用基于RM“奖励”的PPO算法[58]更新整个生成的摘要 ...
For example, summarization models are often trained to predict human reference summaries and evaluated using ROUGE, but both of these metrics are rough proxies for what we really care about---summary quality. In this work, we show that it is possible to significantly improve summary quality by ...
Learning to Summarize from Human Feedback This repository contains code to run our models, including the supervised baseline, the trained reward model, and the RL fine-tuned policy. Supported platform: Python 3.7 64-bit on Ubuntu 18.04
OpenAI 推出的 ChatGPT 对话模型掀起了新的 AI 热潮,它面对多种多样的问题对答如流,似乎已经打破了机器和人的边界。这一工作的背后是大型语言模型 (Large Language Model,LLM) 生成领域的新训练范式:RLHF (Reinforcement Learning from Human Feedback) ,即以强化学习方式依据人类反馈优化语言模型。
这个工作用到的RLHF(Reinforcement Learning from Human Feedback)是ChatGPT系列背后一个很重要的方法。如何把人类的偏好告诉生成式模型?传统的通过监督学习和最大化对数似然并不是一个好的方法。因为在摘要、翻译等任务中,可能同时有几个大相径庭的版本都是好的输出,而最大化对数似然只会迫使输出去接近训练集里的...
good as the human reference without any news-specific fine-tuning. 2 We conduct extensive analyses to understand our human feedback dataset and fine-tuned models.3 We establish that ourreward modelgeneralizes to newdatasets, and that optimizing our reward model results in better summaries than ...