Tracking the perspectives of interacting language models
arxiv(2024)
Abstract
Large language models (LLMs) are capable of producing high quality
information at unprecedented rates. As these models continue to entrench
themselves in society, the content they produce will become increasingly
pervasive in databases that are, in turn, incorporated into the pre-training
data, fine-tuning data, retrieval data, etc. of other language models. In this
paper we formalize the idea of a communication network of LLMs and introduce a
method for representing the perspective of individual models within a
collection of LLMs. Given these tools we systematically study information
diffusion in the communication network of LLMs in various simulated settings.
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