Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: An Adversarial Benchmark for Fake News Detection Models   With the proliferation of online misinformation, fake news detection has
gained importance in the artificial intelligence community. In this paper, we
propose an adversarial benchmark that tests the ability of fake news detectors
to reason about real-world facts. We formulate adversarial attacks that target
three aspects of "understanding": compositional semantics, lexical relations,
and sensitivity to modifiers. We test our benchmark using BERT classifiers
fine-tuned on the LIAR arXiv:arch-ive/1705648 and Kaggle Fake-News datasets,
and show that both models fail to respond to changes in compositional and
lexical meaning. Our results strengthen the need for such models to be used in
conjunction with other fact checking methods.

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The categories of this paper are: alg-geom
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
22
The categories of this paper are: chao-dyn
4
The categories of this paper are: cond-mat
35
The categories of this paper are: mtrl-th
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
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The categories of this paper are: comp-gas
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The categories of this paper are: ao-sci
2
The categories of this paper are: math
15
The categories of this paper are: nucl-ex
11
The categories of this paper are: hep-lat
34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th