On the Amplification of Linguistic Bias through Unintentional
  Self-reinforcement Learning by Generative Language Models -- A Perspective
Minhyeok Lee
Generative Language Models (GLMs) have the potential to significantly shape
our linguistic landscape due to their expansive use in various digital
applications. However, this widespread adoption might inadvertently trigger a
self-reinforcement learning cycle that can amplify existing linguistic biases.
This paper explores the possibility of such a phenomenon, where the initial
biases in GLMs, reflected in their generated text, can feed into the learning
material of subsequent models, thereby reinforcing and amplifying these biases.
Moreover, the paper highlights how the pervasive nature of GLMs might influence
the linguistic and cognitive development of future generations, as they may
unconsciously learn and reproduce these biases. The implications of this
potential self-reinforcement cycle extend beyond the models themselves,
impacting human language and discourse. The advantages and disadvantages of
this bias amplification are weighed, considering educational benefits and ease
of future GLM learning against threats to linguistic diversity and dependence
on initial GLMs. This paper underscores the need for rigorous research to
understand and address these issues. It advocates for improved model
transparency, bias-aware training techniques, development of methods to
distinguish between human and GLM-generated text, and robust measures for
fairness and bias evaluation in GLMs. The aim is to ensure the effective, safe,
and equitable use of these powerful technologies, while preserving the richness
and diversity of human language.