The Impact of Revealing Large Language Model Stochasticity on Trust, Reliability, and Anthropomorphization

Abstract

Interfaces for interacting with large language models (LLMs) are often designed to mimic human conversations, typically presenting a single response to user queries. This design choice can obscure the probabilistic and predictive nature of these models, potentially fostering undue trust and over-anthropomorphization of the underlying model. In this paper, we investigate (i) the effect of displaying multiple responses simultaneously as a countermeasure to these issues, and (ii) how a cognitive support mechanism-highlighting structural and semantic similarities across responses-helps users deal with the increased cognitive load of that intervention. We conducted a within-subjects study in which participants inspected responses generated by an LLM under three conditions: one response, ten responses with cognitive support, and ten responses without cognitive support. Participants then answered questions about workload, trust and reliance, and anthropomorphization. We conclude by reporting the results of these studies and discussing future work and design opportunities for future LLM interfaces.