  The practice of Retrieval-Augmented Generation (RAG), which integrates Large
Language Models (LLMs) with retrieval systems, has become increasingly
prevalent. However, the repercussions of LLM-derived content infiltrating the
web and influencing the retrieval-generation feedback loop are largely
uncharted territories. In this study, we construct and iteratively run a
simulation pipeline to deeply investigate the short-term and long-term effects
of LLM text on RAG systems. Taking the trending Open Domain Question Answering
(ODQA) task as a point of entry, our findings reveal a potential digital
"Spiral of Silence" effect, with LLM-generated text consistently outperforming
human-authored content in search rankings, thereby diminishing the presence and
impact of human contributions online. This trend risks creating an imbalanced
information ecosystem, where the unchecked proliferation of erroneous
LLM-generated content may result in the marginalization of accurate
information. We urge the academic community to take heed of this potential
issue, ensuring a diverse and authentic digital information landscape.
