WebGPT: Browser-assisted question-answering with human feedback

Authors:
  • RNReiichiro Nakano
    Reiichiro Nakano
  • JHJacob Hilton
    Jacob Hilton
  • SBSuchir Balaji
    Suchir Balaji
  • JWJeff Wu
    Jeff Wu
  • LOLong Ouyang
    Long Ouyang
  • CKChristina Kim
    Christina Kim
  • CHChristopher Hesse
    Christopher Hesse
  • SJShantanu Jain
    Shantanu Jain
  • VKVineet Kosaraju
    Vineet Kosaraju
  • WSWilliam Saunders
    William Saunders
  • XJXu Jiang
    Xu Jiang
  • KCKarl Cobbe
    Karl Cobbe
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Abstract
We fine-tune GPT-3 to answer long-form questions using a text-based web-browsing environment, which allows the model to search and navigate the web. By setting up the task so that it can be performed by humans, we are able to train models on the task using imitation learning, and then optimize answer quality with human feedback. To make human evaluation of factual accuracy easier, models must collect references while browsing in support of their answers. We train and evaluate our models on ELI5, a dataset of questions asked by Reddit users. Our best model is obtained by fine-tuning GPT-3 using behavior cloning, and then performing rejection sampling against a reward model trained to predict human preferences. This model's answers are preferred by humans 56% of the time to those of our human demonstrators, and 69% of the time to the highest-voted answer from Reddit.