Tracking the perspectives of interacting language models

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. The success of large pre-trained models in natural language processing (Devlin et al., 2018), computer vision (Oquab et al., 2023), signal processing (Radford et al., 2023), among other domains (Jumper et al., 2021) across various computing and human benchmarks has brought them to the forefront of the technology-centric world. Given their ability to produce human-expert level responses for a large set of knowledge-based questions (Touvron et al., 2023; Achiam et al., 2023), the content they produce is often propagated throughout forums that have influence over other models and human users (Brinkmann et al., 2023). As such, it is important to develop sufficient frameworks and complementary tools to understand how information produced by these models affects the behavior of other models and human users. We refer to a system where a model can potentially influence other models as a system of interacting language models.