The Impact of Response Latency and Task Type on Human-LLM Interaction and Perception

TL;DR

This study systematically explores how response latency affects user perception and interaction with large language models across different task types, revealing nuanced effects on perceived output quality and user behavior.

Contribution

It provides empirical evidence on the impact of latency on user perception and interaction, highlighting latency as a tunable design parameter with ethical considerations.

Findings

Shorter latencies led to lower ratings of thoughtfulness and usefulness.

User interaction behaviors were consistent across different latencies.

Task type influenced prompting frequency and perception of delays.

Abstract

Responsiveness in large language model (LLM) applications is widely assumed to be critical, yet the impact of latency on user behavior and perception of output quality has not been systematically explored. We report a controlled experiment varying time-to-first-token latency (2, 9, 20 seconds) across two taxonomy-driven knowledge task types (Creation and Advice). Log analyses reveal that user interaction behaviors were robust to latency, yet varied by task type: Creation tasks elicited more frequent prompting than Advice tasks. In contrast, participants who experienced 2-second latencies rated the LLM's outputs less thoughtful and useful than those who experienced 9- or 20-second latencies. Participants attributed delays to AI deliberation, though long waits occasionally shifted this interpretation toward frustration or concerns about reliability. Overall, this work demonstrates that latency is not simply a cost to reduce but a tunable design variable with ethical implications. We offer design strategies for enhancing human-LLM interaction.