WebGPT: Browser-assisted question-answering with human feedback
The first time the loop reached outside itself. WebGPT gives a model a browser and human feedback on how it used one, which turns retrieval from a preprocessing step into an action the model chooses to take.
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A text-based browsing environment turns search into an in-loop action with observations.
Human preference on the whole trajectory, not just the answer, becomes the training signal.
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.