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Long Ouyang

2 accepted papers

2022

Training language models to follow instructions with human feedback

NeurIPS 2022accept

Making language models bigger does not inherently make them better at following a user's intent. For example, large language models can generate outputs that are untruthful, toxic, or simply not helpful to the user. In other words, these models are not aligned with their users. In this paper, we sho…

2020

Learning to summarize with human feedback

NeurIPS 2020poster

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…