#summarization #gpt3 #openai
Text Summarization is a hard task, both in training and evaluation. Training is usually done maximizing the log-likelihood of a human-generated reference summary, while evaluation is performed using overlap-based metrics like ROUGE. Both significantly undervalue the breadth and intricacies of language and the nature of the information contained in text summaries. This paper by OpenAI includes direct human feedback both in evaluation and – via reward model proxies – in training. The final model even outperforms single humans when judged by other humans and is an interesting application of using reinforcement learning together with humans in the loop.
OUTLINE:
0:00 – Intro & Overview
5:35 – Summarization as a Task
7:30 – Problems with the ROUGE Metric
10:10 – Training Supervised Models
12:30 – Main Results
16:40 – Including Human Feedback with Reward Models & RL
26:05 – The Unknown Effect of Better Data
28:30 – KL Constraint & Connection to Adversarial Examples
37:15 – More Results
39:30 – Understanding the Reward Model
41:50 – Limitations & Broader Impact
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Abstract:
As language models become more powerful, training and evaluation are increasingly bottlenecked by the data and metrics used for a particular task. 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 training a model to optimize for human preferences. We 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 significantly outperform both human reference summaries and much larger models fine-tuned with supervised learning alone. Our models also transfer to CNN/DM news articles, producing summaries nearly as good as the human reference without any news-specific fine-tuning. We 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 learning researchers to pay closer attention to how their training loss affects the model behavior they actually want.
Authors: Nisan Stiennon, Long Ouyang, Jeff Wu, Daniel M. Ziegler, Ryan Lowe, Chelsea Voss, Alec Radford, Dario Amodei, Paul Christiano
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