MiniAppBench: Evaluating the Shift from Text to Interactive HTML Responses in LLM-Powered Assistants
This addresses the need for better benchmarks in AI-human interaction as LLMs shift from text to interactive responses, though it is incremental as it builds on existing evaluation methods.
The paper tackles the problem of evaluating LLMs' ability to generate interactive HTML applications (MiniApps) by introducing MiniAppBench, a benchmark with 500 tasks across six domains, and MiniAppEval, an agentic evaluation framework that shows high alignment with human judgment.
With the rapid advancement of Large Language Models (LLMs) in code generation, human-AI interaction is evolving from static text responses to dynamic, interactive HTML-based applications, which we term MiniApps. These applications require models to not only render visual interfaces but also construct customized interaction logic that adheres to real-world principles. However, existing benchmarks primarily focus on algorithmic correctness or static layout reconstruction, failing to capture the capabilities required for this new paradigm. To address this gap, we introduce MiniAppBench, the first comprehensive benchmark designed to evaluate principle-driven, interactive application generation. Sourced from a real-world application with 10M+ generations, MiniAppBench distills 500 tasks across six domains (e.g., Games, Science, and Tools). Furthermore, to tackle the challenge of evaluating open-ended interactions where no single ground truth exists, we propose MiniAppEval, an agentic evaluation framework. Leveraging browser automation, it performs human-like exploratory testing to systematically assess applications across three dimensions: Intention, Static, and Dynamic. Our experiments reveal that current LLMs still face significant challenges in generating high-quality MiniApps, while MiniAppEval demonstrates high alignment with human judgment, establishing a reliable standard for future research. Our code is available in github.com/MiniAppBench.