Societal Biases in Language Generation: Progress and Challenges
- URL: http://arxiv.org/abs/2105.04054v1
- Date: Mon, 10 May 2021 00:17:33 GMT
- Title: Societal Biases in Language Generation: Progress and Challenges
- Authors: Emily Sheng, Kai-Wei Chang, Premkumar Natarajan, and Nanyun Peng
- Abstract summary: Language generation presents unique challenges in terms of direct user interaction and the structure of decoding techniques.
We present a survey on societal biases in language generation, focusing on how techniques contribute to biases and on progress towards bias analysis and mitigation.
Motivated by a lack of studies on biases from decoding techniques, we also conduct experiments to quantify the effects of these techniques.
- Score: 43.06301135908934
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Technology for language generation has advanced rapidly, spurred by
advancements in pre-training large models on massive amounts of data and the
need for intelligent agents to communicate in a natural manner. While
techniques can effectively generate fluent text, they can also produce
undesirable societal biases that can have a disproportionately negative impact
on marginalized populations. Language generation presents unique challenges in
terms of direct user interaction and the structure of decoding techniques. To
better understand these challenges, we present a survey on societal biases in
language generation, focusing on how techniques contribute to biases and on
progress towards bias analysis and mitigation. Motivated by a lack of studies
on biases from decoding techniques, we also conduct experiments to quantify the
effects of these techniques. By further discussing general trends and open
challenges, we call to attention promising directions for research and the
importance of fairness and inclusivity considerations for language generation
applications.
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