MapSatisfyBench: Benchmarking Satisfaction-Aware Map Agents through Behavior-Grounded Implicit Decision Factors
arXiv:2606.17453v1 Announce Type: new Abstract: Large language model agents are increasingly integrated into map services. Since map services are embedded in everyday-life scenarios rather than professional task settings, users often express their needs informally, resulting in underspecified queries with many unspoken needs, namely, implicit decision factors that are critical for user satisfaction. Although clarification is an effective way to mitigate this issue, it increases user burden in daily interaction, and a capable agent should first proactively recover such factors from available in
The proliferation of Large Language Models (LLMs) in everyday applications necessitates new benchmarking methods to evaluate their performance against nuanced human expectations.
Evaluating LLM agents in map services by 'satisfaction-aware' metrics highlights a crucial next step in making AI assistants truly useful and intuitive for real-world, informal user needs.
The focus for AI agent development shifts from mere task completion to understanding and proactively addressing 'implicit decision factors' for user satisfaction, requiring more sophisticated behavioral grounding.
- · AI agent developers
- · Map service providers
- · Consumers of AI-powered services
- · Developers focused solely on explicit queries
- · Static, non-adaptive map services
Improved user experience in AI-powered map and navigation services due to more intuitive and proactive agents.
Increased adoption and reliance on AI agents for daily tasks, as they become better at anticipating unstated needs.
The methodology for benchmarking implicit decision factors could extend to other complex, human-centric AI applications, leading to more human-aligned AI generally.
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Read at arXiv cs.AI