On the Amplification of Linguistic Bias through Unintentional Self-reinforcement Learning by Generative Language Models -- A Perspective

TL;DR

This paper discusses how generative language models might unintentionally reinforce and amplify existing linguistic biases through a self-reinforcement cycle, impacting future language use and societal discourse.

Contribution

It introduces the concept of bias amplification in GLMs, highlighting potential feedback loops and emphasizing the need for bias mitigation and transparency techniques.

Findings

Bias in GLMs can feed into subsequent models, amplifying linguistic biases.

GLMs influence linguistic and cognitive development, potentially reinforcing societal biases.

Addressing bias in GLMs is crucial for preserving linguistic diversity and fairness.

Abstract

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.