The Impact of Response Latency and Task Type on Human-LLM Interaction and Perception
This work addresses the design of human-LLM interactions for developers and users, showing that latency can be a tunable variable with ethical implications, though it is incremental in exploring known assumptions.
The study investigated how response latency and task type affect human interaction with and perception of LLMs, finding that while user behavior was robust to latency, participants rated outputs as less thoughtful and useful at 2-second latencies compared to 9- or 20-second latencies.
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.