Generative Echo Chamber? Effects of LLM-Powered Search Systems on Diverse Information Seeking
CoRR(2024)
摘要
Large language models (LLMs) powered conversational search systems have
already been used by hundreds of millions of people, and are believed to bring
many benefits over conventional search. However, while decades of research and
public discourse interrogated the risk of search systems in increasing
selective exposure and creating echo chambers – limiting exposure to diverse
opinions and leading to opinion polarization, little is known about such a risk
of LLM-powered conversational search. We conduct two experiments to
investigate: 1) whether and how LLM-powered conversational search increases
selective exposure compared to conventional search; 2) whether and how LLMs
with opinion biases that either reinforce or challenge the user's view change
the effect. Overall, we found that participants engaged in more biased
information querying with LLM-powered conversational search, and an opinionated
LLM reinforcing their views exacerbated this bias. These results present
critical implications for the development of LLMs and conversational search
systems, and the policy governing these technologies.
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