Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: DuTrust: A Sentiment Analysis Dataset for Trustworthiness Evaluation   While deep learning models have greatly improved the performance of most
artificial intelligence tasks, they are often criticized to be untrustworthy
due to the black-box problem. Consequently, many works have been proposed to
study the trustworthiness of deep learning. However, as most open datasets are
designed for evaluating the accuracy of model outputs, there is still a lack of
appropriate datasets for evaluating the inner workings of neural networks. The
lack of datasets obviously hinders the development of trustworthiness research.
Therefore, in order to systematically evaluate the factors for building
trustworthy systems, we propose a novel and well-annotated sentiment analysis
dataset to evaluate robustness and interpretability. To evaluate these factors,
our dataset contains diverse annotations about the challenging distribution of
instances, manual adversarial instances and sentiment explanations. Several
evaluation metrics are further proposed for interpretability and robustness.
Based on the dataset and metrics, we conduct comprehensive comparisons for the
trustworthiness of three typical models, and also study the relations between
accuracy, robustness and interpretability. We release this trustworthiness
evaluation dataset at \url{https://github/xyz} and hope our work can facilitate
the progress on building more trustworthy systems for real-world applications.

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The categories of this paper are: hep-ph
20
The categories of this paper are: acc-phys
23
The categories of this paper are: solv-int
29
The categories of this paper are: funct-an
37
The categories of this paper are: supr-con
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
19
The categories of this paper are: eess
24
The categories of this paper are: dg-ga
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The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
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The categories of this paper are: stat