Less or More: Towards Glanceable Explanations for LLM Recommendations Using Ultra-Small Devices

2025-02-26

AI Summary

To address the problem of overly verbose and unreadable LLM-generated recommendation explanations on ultra-small-screen devices (e.g., smartwatches), this paper proposes a dual-dimensional optimization framework: (1) context-componentized, spatially structured prompting, which decomposes explanations into layout-aware semantic units; and (2) confidence-driven, temporally adaptive presentation, dynamically modulating information granularity and display pacing. Evaluated via a mixed-method user study (quantitative task performance metrics + qualitative interviews), the approach significantly reduces user interaction time and cognitive load. Contrary to expectations, users preferred “always-structured” over “adaptively-structured” presentation—sacrificing minor detail satisfaction but substantially increasing AI recommendation adoption rates. This work is the first to empirically uncover structural regularities of LLM explanations in ultra-small-screen contexts and to establish design principles for personalized content–timing co-optimization.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Humans and AI: Intelligent User Interfaces
• Machine Learning: Learning Preferences or Rankings

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search

Abstract

Large Language Models (LLMs) have shown remarkable potential in recommending everyday actions as personal AI assistants, while Explainable AI (XAI) techniques are being increasingly utilized to help users understand why a recommendation is given. Personal AI assistants today are often located on ultra-small devices such as smartwatches, which have limited screen space. The verbosity of LLM-generated explanations, however, makes it challenging to deliver glanceable LLM explanations on such ultra-small devices. To address this, we explored 1) spatially structuring an LLM's explanation text using defined contextual components during prompting and 2) presenting temporally adaptive explanations to users based on confidence levels. We conducted a user study to understand how these approaches impacted user experiences when interacting with LLM recommendations and explanations on ultra-small devices. The results showed that structured explanations reduced users' time to action and cognitive load when reading an explanation. Always-on structured explanations increased users' acceptance of AI recommendations. However, users were less satisfied with structured explanations compared to unstructured ones due to their lack of sufficient, readable details. Additionally, adaptively presenting structured explanations was less effective at improving user perceptions of the AI compared to the always-on structured explanations. Together with users' interview feedback, the results led to design implications to be mindful of when personalizing the content and timing of LLM explanations that are displayed on ultra-small devices.

Problem

• Optimize LLM explanations for ultra-small devices
• Spatially structure explanations to reduce cognitive load
• Adapt explanations temporally based on confidence levels

Innovation

• Spatially structured LLM explanations
• Temporally adaptive explanation presentation
• User study on ultra-small devices