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    <responseDate>2026-10-12T19:01:17Z</responseDate>
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            <header>
                <identifier>oai:data.mendeley.com/xwjsgfzh83.1</identifier>
                <datestamp>2025-11-12T11:34:43Z</datestamp>
            </header>
            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Tawfik, Mohammed</dc:creator>
    <dc:title> Saudi ESB Dataset</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains 10,000 synthetic samples for training and evaluating AI systems 
for Saudi Arabia&apos;s End-of-Service Benefits (ESB) calculation under Saudi Labour Law 
(Royal Decree M/51, 2005; amended 2015). The dataset systematically models real-world 
legal consultation complexities absent from existing legal AI benchmarks.

KEY FEATURES:
- 10,000 query-response pairs (8,000 train / 1,000 validation / 1,000 test)
- Six complexity tiers: Standard cases (60%), Incomplete information (15%), Conflicting 
  evidence (10%), Legal interpretation (5%), Multi-step reasoning (5%), Adversarial (5%)
- Explicit uncertainty modeling with confidence scores (0-1 scale)
- Coverage: 16 Saudi Labour Law articles (74-88, 137-138, 234), 35 termination scenarios
- 2,000 multi-turn conversations (20% of dataset)
- Empirically grounded: Distributions derived from 47,382 real ESB cases (Saudi Ministry 
  of Human Resources, 2019-2023), 3,847 labor court disputes, and HR consultant interviews (n=23)

DATA STRUCTURE:
Each sample includes:
- Query: Natural language employee profile with service years, salary, termination type
- Ground truth: ESB amount (SAR), applicable legal articles, calculation steps
- Confidence score: 0-1 scale reflecting query ambiguity
- Complexity tier: 1-6 classification
- Metadata: Employee demographics, termination scenario, multi-turn conversation flag

VALIDATION:
- 97.3% pass rate on 12 automated validation checks
- Expert validation by Saudi labor law professionals
- Stratified sampling ensures representativeness across termination scenarios

APPLICATIONS:
- Training legal AI systems for ESB calculation
- Benchmarking uncertainty quantification methods
- Evaluating robustness to incomplete information and adversarial inputs
- Research on parameter-efficient fine-tuning and retrieval-augmented generation

LIMITATIONS:
- Synthetic data (not actual legal cases) due to privacy constraints
- Focused on ESB calculation only (16 of 245 Saudi Labour Law articles)
- Requires domain expertise for interpretation and application



LICENSE: CC BY 4.0 (recommended - allows reuse with attribution)

DATA FORMAT: JSON Lines (.jsonl) with structured fields for programmatic access</dc:description>
    <dc:subject>Human Resource</dc:subject>
    <dc:subject>Large Language Model</dc:subject>
    <dc:contributor>Aldosari, Nasser </dc:contributor>
    <dc:contributor> Hussain, Farookh</dc:contributor>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/xwjsgfzh83.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/xwjsgfzh83.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/xwjsgfzh83</dc:relation>
    <dc:date>2025-11-12T11:34:43Z</dc:date>
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