  We study the evolution of opinions inside a population of interacting large
language models (LLMs). Every LLM needs to decide how much funding to allocate
to an item with three initial possibilities: full, partial, or no funding. We
identify biases that drive the exchange of opinions based on the LLM's tendency
to (i) find consensus with the other LLM's opinion, (ii) display caution when
specifying funding, and (iii) consider ethical concerns in its opinion. We find
these biases are affected by the perceived absence of compelling reasons for
opinion change, the perceived willingness to engage in discussion, and the
distribution of allocation values. Moreover, tensions among biases can lead to
the survival of funding for items with negative connotations. We also find that
the final distribution of full, partial, and no funding opinions is more
diverse when an LLM freely forms its opinion after an interaction than when its
opinion is a multiple-choice selection among the three allocation options. In
the latter case, consensus or polarization is generally attained. When agents
are aware of past opinions, they seek to maintain consistency with them, and
more diverse updating rules emerge. Our study is performed using a Llama 3 LLM.
