Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: WebGPT: Browser-assisted question-answering with human feedback   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.

2
The categories of this paper are: math
36
The categories of this paper are: cmp-lg
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
23
The categories of this paper are: solv-int
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
11
The categories of this paper are: hep-lat
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
25
The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th