GDPVal RealWorks: Core Overview

GDPVal RealWorks is a benchmark platform for assessing large language models (LLMs) on real expert tasks. It provides YAML-driven test workflows and real-time dashboard functionality, supporting the GDPVal Gold Subset dataset tailored to bridge gaps between standard benchmarks and real-world scenarios.

Keywords: large language models, benchmark testing, expert tasks, evaluation platform, YAML configuration, real-time dashboard

Current LLM evaluations rely on standardized benchmarks like MMLU, GSM8K, and HumanEval, but these often disconnect from real-world performance. Key gaps:

• Problem format: Structured vs. open-ended complex tasks
• Domain expertise: General benchmarks lack deep field coverage
• Context complexity: Real tasks require longer contexts
• Evaluation ambiguity: No single correct answer, needing expert judgment

GDPVal Gold Subset addresses these challenges, with GDPVal RealWorks as its evaluation infrastructure.

YAML-Driven Pipeline

• Configurable: Adjust models, prompts, metrics, outputs via YAML (no code changes)
• Reproducible: YAML files record full experiment configs
• Version-friendly: Text format easy to track changes

Real-Time Dashboard

• Progress tracking: Completed/ongoing/pending tasks
• Performance metrics: Real-time scores across task categories
• Error analysis: Failed case classification
• Resource monitoring: Computational usage

This makes evaluation transparent and adjustable.

The dataset’s key characteristics:

• Real sources: Professional exams, work decisions, expert diagnoses
• Expert validation: Clarity, correct answers, and clear scoring standards
• Diverse domains: Covers multiple professional fields to avoid overfitting

Ensures evaluation aligns with real-world professional needs.

Pipeline Stages

1. Data loading from GDPVal Gold Subset
2. Model inference for answer generation
3. Structured answer extraction
4. Auto evaluation (rules/auxiliary models)
5. Expert manual review for difficult cases
6. Result summary report

Concurrency Handling

• Model-level parallel requests
• Batch optimization for supported models
• Rate limit management
• Fault tolerance (auto retries)

Evaluation Metrics

• Accuracy: Exact match, partial match, semantic similarity
• Robustness: Prompt stability, answer consistency
• Efficiency: Response time, token usage

Provides holistic performance insights.

Application Scenarios

• Model selection: Enterprise LLM choice for professional domains
• Capability diagnosis: Identify model weaknesses (e.g., legal reasoning gaps)
• Continuous monitoring: Track performance over iterations
• Research benchmark: Standardized comparison environment

Framework Comparison

Feature	Traditional Script	Commercial Platform	GDPVal RealWorks
Config Flexibility	Low	Medium	High
Real-Time Monitoring	No	Yes	Yes
Cost	Low	High	Low
Open Source	Not necessarily	No	Yes
Real Task Focus	Not necessarily	Not necessarily	Yes

Usage Steps

1. Requirement analysis: Define goals and metrics
2. Data preparation: Gather scenario-matching test data
3. Baseline establishment: Evaluate mainstream models
4. Iterative optimization: Adjust models/prompts
5. Continuous monitoring: Regular performance tracking

Limitations & Future Directions

• Current gaps: Narrow domain coverage, auto evaluation room for improvement, limited multilingual support
• Future plans: Expand datasets, LLM-based auto judges, multilingual enhancement, more model integrations

GDPVal RealWorks advances LLM evaluation toward real expert scenarios. Its YAML pipeline and real-time dashboard lower technical barriers for high-quality assessments.

As AI enters professional fields, reliable evaluation tools are critical. This platform is infrastructure for responsible AI deployment—organizations using LLMs in production should prioritize such capabilities.