GDPVal RealWorks: A Large Language Model Evaluation Framework for Real Professional Tasks

GDPVal RealWorks is a large language model evaluation framework based on YAML configuration pipelines and real-time React dashboards. It focuses on 220 real expert tasks across 11 industries, aiming to address the disconnect between traditional LLM evaluations (such as MMLU and HumanEval) and actual work scenarios. It provides model capability assessments that are more aligned with enterprise deployment needs, helping users make informed decisions on model selection.

There are fundamental issues in the current LLM evaluation field: most benchmarks focus on academic puzzles and standardized tests, which are significantly disconnected from actual work scenarios—models that perform well on general benchmarks may not be competent for professional tasks such as doctor's diagnostic assistance or lawyer's contract review. The GDPVal Gold Subset project is designed to address this pain point, shifting the evaluation paradigm from 'what you know' to 'what you can do', focusing on tasks in real professional environments and being more aligned with enterprise deployment needs.

YAML-Driven Evaluation Pipeline

The core design of the system is 'configuration as evaluation'. Users do not need to write code; instead, they define evaluation tasks through YAML files, which include four parts: task description, input/output specifications, evaluation metrics, and reference standards. This lowers the threshold for customizing evaluation sets and facilitates participation by domain experts and process auditing.

Real-Time React Dashboard

The built-in dashboard provides multi-dimensional visualization: industry-level comparison, task type analysis, real-time progress tracking, and Excel/PDF report export. It helps decision-makers quickly identify models suitable for business scenarios instead of relying on abstract scores.

GDPVal Gold Subset covers 220 real tasks across 11 industries, each derived from actual work scenarios and designed/validated by domain experts:

• Financial Services: Risk assessment, compliance check, investment analysis
• Healthcare: Clinical decision support, medical literature summarization, patient communication
• Legal Compliance: Contract review, regulation interpretation, case retrieval
• Marketing: Content creation, competitor analysis, user profiling
• Engineering Technology: Code review, technical documentation, fault diagnosis
• Education and Training: Curriculum design, homework grading, learning path planning
• Human Resources: Resume screening, interview question generation, performance evaluation
• Customer Service: Ticket classification, response suggestion, sentiment analysis
• Scientific Research: Literature review, experiment design, data analysis
• Government and Public Services: Policy interpretation, public service consultation, public opinion monitoring
• Manufacturing: Quality control, supply chain optimization, predictive maintenance This dataset is fundamentally different from synthetic datasets, as its results directly correspond to actual business value.

GDPVal RealWorks has significant application value:

• Enterprise AI Teams: Provides objective basis for model selection, customizes evaluations based on industry characteristics, and avoids relying on vendor promotions or general rankings;
• Model Developers: Through fine-grained capability diagnosis, accurately identifies model shortcomings and guides training data collection and fine-tuning strategies;
• Academic Research: Promotes the shift of evaluation methodology towards pragmatism and provides methodological references for subsequent research.

The current version has limitations: it is mainly oriented towards the Windows platform, and cross-platform support needs to be improved; evaluation tasks rely on human experts, and automated generation is an improvement direction. A deeper challenge is evaluation timeliness—model capabilities are improving rapidly, so evaluation sets need to be continuously updated to maintain distinguishability, and establishing a sustainable maintenance mechanism is key to long-term development. This framework represents an important step in the evolution of LLM evaluation from 'academic competition' to 'practical tool'.