Why LLMs get dates and times wrong (and how to fix it) (www.cronofy.com)

A recent exploration into the shortcomings of large language models (LLMs) revealed that these AI systems often struggle with understanding and reasoning about time, particularly when managing scheduling tasks. When developing a Scheduling Agent for Slack and Teams, it became evident that while LLMs could process time-related prompts, they consistently made errors when attempting to discern whether events were in the past, present, or future. This flaw stems from the models' inherent lack of temporal understanding and poor reasoning capabilities, highlighting a crucial gap between raw data interpretation and human-like reasoning. To address these challenges, the developers implemented practical fixes, including providing explicit labels indicating whether events were past, present, or future, thereby bypassing complex reasoning. They also ensured that the agent received both UTC timestamps and user-friendly formatted times, facilitating accurate communication. The emphasis is now on limiting the cognitive load placed on the model by not expecting it to draw conclusions from its inputs, thus enhancing its reliability. This approach emphasizes the need for clear interfaces that bridge the gap between machine data processing and human language understanding, underscoring a growing trend in AI development focused on task-specific guidance rather than reliance on abstract reasoning.