Structure-BiEval: A Self-Supervised, Dual-Track Framework for Decoupling Structure and Content in LLM Evaluation for Web Information Systems

1. 1Build a self-supervised framework to decouple structure and content in LLM evaluation
2. 2Run experiments to evaluate the framework's effectiveness in capturing topological differences
3. 3Configure the framework to handle Web-native payloads
4. 4Test the framework's ability to faithfully translate natural language into rigorous structured formats
5. 5Apply the framework to real-world Web information systems

Data scientists and AI engineers on a team benefit from this framework as it helps evaluate LLMs' ability to translate natural language into structured formats, which is critical for Web API invocation and data exchange

💡 Traditional text metrics are insufficient for evaluating LLMs' structural fidelity, a self-supervised framework is needed

🚀 Evaluate LLMs' structural fidelity in Web info systems with Structure-BiEval! 🤖