One vs. Many: Comprehending Accurate Information from Multiple Erroneous and Inconsistent AI Generations

TL;DR

This study explores how presenting users with multiple inconsistent AI-generated outputs affects their understanding and perception, revealing that inconsistencies can enhance comprehension and transparency about AI limitations.

Contribution

It introduces a categorization of output inconsistencies and demonstrates that multiple, conflicting LLM outputs can improve user comprehension and awareness of AI limitations.

Findings

Inconsistencies reduce perceived AI capacity.

Multiple outputs increase user comprehension.

Two passages yield the most significant benefits.

Abstract

As Large Language Models (LLMs) are nondeterministic, the same input can generate different outputs, some of which may be incorrect or hallucinated. If run again, the LLM may correct itself and produce the correct answer. Unfortunately, most LLM-powered systems resort to single results which, correct or not, users accept. Having the LLM produce multiple outputs may help identify disagreements or alternatives. However, it is not obvious how the user will interpret conflicts or inconsistencies. To this end, we investigate how users perceive the AI model and comprehend the generated information when they receive multiple, potentially inconsistent, outputs. Through a preliminary study, we identified five types of output inconsistencies. Based on these categories, we conducted a study (N=252) in which participants were given one or more LLM-generated passages to an information-seeking question. We found that inconsistency within multiple LLM-generated outputs lowered the participants' perceived AI capacity, while also increasing their comprehension of the given information. Specifically, we observed that this positive effect of inconsistencies was most significant for participants who read two passages, compared to those who read three. Based on these findings, we present design implications that, instead of regarding LLM output inconsistencies as a drawback, we can reveal the potential inconsistencies to transparently indicate the limitations of these models and promote critical LLM usage.