Datasets:

Cornada
/

ads-mm-benchmark

license: cc-by-4.0
task_categories:
  - text-classification
  - sentence-similarity
  - zero-shot-classification
language:
  - en
  - ru
  - sv
size_categories:
  - 10K<n<100K
tags:
  - education
  - labor-market
  - occupational-classification
  - multilingual
  - benchmark
  - cross-national
  - temporal-drift
  - embedding-evaluation
pretty_name: 'ADS: A Multilingual Educational Benchmark with Cross-Market Evaluation'

ADS: A Multilingual Educational Benchmark with Cross-Market Evaluation

Dataset Description

ADS is an evaluation benchmark spanning 40,552 records from 14 universities across 4 countries and 4 labor market classification systems in 3 languages (English, Russian, Swedish).

Dataset Summary

The benchmark tests whether independently designed national classification systems converge in a shared embedding space and what that space reveals about the structure of education and work.

Key findings:

Cross-system occupational convergence: Recall@1=0.718 without task-specific training
Heterogeneous temporal drift: 44.2% zero drift, 37.0% single-direction, 18.8% multi-directional
AI exposure prediction: R²=0.653 from frozen embeddings; job growth correctly unpredictable (R²=0.07)

Languages

English, Russian, Swedish

Data Instances

Each record contains a canonical ID, type (course/occupation/competency/mission), institution, title, text description, and metadata including country, language, and provenance information.

Data Fields

canonical_id: Unique identifier
type: COURSE | OCCUPATION | COMPETENCY | MISSION
institution: Source institution (e.g., MIT, MISIS, KTH)
title: Record title
text: Full text description
metadata: JSON with institution, country, provenance

Data Splits

Five benchmark tasks with deterministic train/dev/test splits:

Task	Train	Dev	Test	Description
Cross-market occupation matching	varies	varies	varies	Match occupations across O*NET, ESCO, ProfStandart, SSYK
Cross-lingual alignment	varies	varies	varies	Align same occupations across EN/RU/SV
AI exposure prediction	varies	varies	varies	Predict automation exposure from text
Temporal drift prediction	varies	varies	varies	Predict occupation drift direction
Video availability prediction	varies	varies	varies	Predict multimedia availability

Source Data

Universities (14): MIT OCW, UC Berkeley, Stanford, KTH, Chalmers, MISIS, HSE, MIPT, ITMO, MEPhI, BMSTU, Open Education (7 Russian universities)
Labor markets (4): O*NET (US), ESCO (EU), ProfStandart (Russia), SSYK (Sweden)
Temporal versions: O*NET 2022, 2023, 2024, 2025; ESCO v1.1.1, v1.2

Responsible AI (RAI) Documentation

Limitations and Constraints

University course data is limited to publicly available syllabi and course descriptions
Russian university data is primarily from engineering and technical institutions (MISIS, MEPhI, BMSTU)
Swedish coverage limited to KTH and Chalmers (technical universities)
Labor market classifications reflect national policy perspectives which may embed cultural biases

Known or Suspected Biases

STEM overrepresentation due to technical university focus
English-language bias in embedding models (affects cross-lingual comparisons)
O*NET reflects US-centric occupational structure

Personal and Sensitive Information

No personal data. All records are institutional/occupational descriptions
No student data, no individual outcomes
Course descriptions are publicly available institutional content

Validated Use Cases

Evaluating cross-lingual and cross-system embedding alignment
Studying temporal drift in occupational classifications
Benchmarking education-labor market alignment methods
Testing what frozen text representations encode about labor markets

Social Impact

May inform educational policy and curriculum design
Cross-national comparisons should be interpreted with awareness of cultural and economic context differences
AI exposure predictions reflect current capabilities, not normative recommendations

Synthetic Data

This dataset contains no synthetic data. All records are sourced from real institutional and governmental classification systems.

Source Dataset Provenance

All source data is publicly available:

MIT OCW: Creative Commons license
O*NET: Public domain (US Department of Labor)
ESCO: EU Open Data
ProfStandart: Russian Ministry of Labor (public)
SSYK: Statistics Sweden (public)
Open Education platform courses: publicly available

Collection and Preprocessing

Course data collected via public APIs and web scraping of official university platforms
Occupational data downloaded from official government sources
Text standardized to UTF-8, deduplicated by content hash
Embeddings computed using 5 frozen encoders (detailed in paper)

Citation

@inproceedings{ads2026,
  title={ADS: A Multilingual Educational Benchmark with Cross-Market Evaluation},
  author={Anonymous},
  booktitle={NeurIPS 2026 Evaluations and Datasets Track},
  year={2026}
}