Qusto/adapta-migrant-onboarding

AdaptaAI: Multilingual RAG Benchmark for Labor Migration Onboarding

Dataset Summary

This dataset is a multilingual question-answering benchmark designed to evaluate Retrieval-Augmented Generation (RAG) systems in the domain of Indian labor migration to Russia. It covers the full onboarding lifecycle: visa and work permit procedures, housing, salary and contracts, SIM card registration, money transfers, healthcare, language requirements, and worker rights and safety.

The dataset contains 257 question-answer pairs spanning English, Russian, Hindi (Devanagari), and Hindi (Roman transliteration). Questions were collected from three source tiers: authentic community posts (Reddit r/AskARussian, r/Moscow, r/GeopoliticsIndia, r/IWantOut, r/expats; YouTube Hindi-language videos), official government and financial FAQ pages, and analytical reviews of the migration domain. The accompanying corpus of 28 documents in Russian provides the knowledge base against which RAG systems are evaluated.

A key feature of this dataset is the anti-hallucination test dimension: 20% of questions (questions marked answerable: false) have no correct answer derivable from the provided corpus. These probes test whether a RAG system correctly says "I don't know" rather than confabulating an answer. The benchmark includes RAGAS metrics from ablation experiments comparing GigaChat, Qwen 2.5-72B, and Qwen3-235B generators across Hindi and Russian query languages.

Languages

The dataset covers four language variants:

Language	Code	Count	Description
English	en	225	Primary language; scraped from Reddit and official sites
Hindi (Devanagari)	hi	16	Authentic questions from YouTube Hindi comments
Hindi (Roman)	hi_roman	5	Hindi written in Latin script (Hinglish), common in worker communities
Russian	ru	11	Questions from Russian-language official sources

The corpus documents are all in Russian. The RAG pipeline under test performs: user language → Russian → retrieval → Russian → user language. This asymmetry is intentional and reflects real-world deployment conditions.

Dataset Structure

Files

data/
  qa.jsonl              — full 257-record Q&A dataset
  qa_train.jsonl        — training split (~206 records, 80%)
  qa_test.jsonl         — test split (~51 records, 20%)

corpus/
  daily_life_adaptation_2026.md
  documents_visa_workpermit_2026.md
  dormitory_rules.md
  emergency_mea_numbers.md
  healthcare_medcommission_2026.md
  housing_options_2026.md
  hr_guide_india_workers.md
  kuper_migrant.md
  migration_amina_app.md
  migration_registration_2026.md
  money_transfer_channels_2026.md
  money_transfer_india_2026.md
  money_transfer_kwikpay.md
  partner_card.md         — synthetic
  partner_dms.md          — synthetic
  partner_deposit.md      — synthetic
  partner_health.md       — synthetic
  partner_mobile.md       — synthetic
  partner_salary.md       — synthetic
  russian_language_exam_2026.md
  safety_construction_site.md
  safety_memo_foreign_workers.md
  safety_rights_madad_2026.md
  salary_tax_mrot_2026.md
  sim_card_gosuslugi_ruid_2026.md
  sim_card_megafon.md
  sim_card_mmc_guide.md
  work_patent_2026.md

benchmark/
  ragas_metrics.json      — RAGAS ablation results

Q&A Record Fields

Each line in qa.jsonl is a JSON object with the following fields:

Field	Type	Description
id	string	Unique identifier, e.g. q001–q257
question	string	Original question text in its source language
category	string	Thematic category (see below)
language	string	Source language: en, ru, hi, hi_roman
question_type	string	Question type: factual, procedural, comparison, troubleshooting, out_of_domain
difficulty	string	easy, medium, or hard
answerable	boolean	true = answer derivable from corpus; false = anti-hallucination probe
answer_hint	string	Expected answer summary for ground truth evaluation
source_type	string	Provenance category (see below)

Categories

Category	Description
documents	Visas, work permits, migration registration, residence status
salary_contract	Employment contracts, wages, taxes, overtime
housing	Employer-provided dormitories, private rental
sim_card	SIM card registration, SNILS, Gosuslugi, biometrics, RuID
money_transfer	Russia–India money transfer channels
healthcare	VHI/DMS insurance, medical commission, emergency care
russian_language	Language exam requirements, exemptions, learning resources
safety_rights	Worker rights, emergency contacts, MADAD portal
partner_products	RAG anti-hallucination probes: questions about partner bank products not in public corpus
daily_life	Climate, food, Indian community, cultural adaptation

Source Types

source_type	Meaning
advisory_publication	Advisory documents from the original system's knowledge base
community_forum	Scraped from Reddit communities
video_community	From YouTube Hindi video comments
analytical_review	Synthesized from domain analytical reviews
official_guide	Official government, consular, or licensed service guides
synthetic	Synthetically generated for anti-hallucination or comparison testing

Data Splits

The dataset uses a stratified 80/20 train/test split by language to preserve language distribution across splits.

Split	Records	en	ru	hi	hi_roman
train	~206	~180	~9	~13	~4
test	~51	~45	~2	~3	~1

Stratification ensures each language variant is represented in both splits. The split is deterministic (seed=42).

Source and Collection Methodology

Questions were collected through three tiers:

Tier 1 — Community sources (authentic questions). Reddit OAuth2 API search across r/AskARussian, r/Moscow, r/GeopoliticsIndia, r/IWantOut, r/expats using 15 India+Russia+work/visa query combinations. 324 raw posts retrieved; 195 retained after filtering out political discussions and irrelevant content. Three YouTube Hindi-language videos about Indian workers in Russia were analyzed to extract 15 community comments.

Tier 2 — Official FAQs and guides. Structured extraction from russiable.com (SIM card guide, money transfer guide), visitrf.com (Gosuslugi for foreigners), sodeistvie.info (work permit for Indian nationals), and e-immigration.ru (warehouse work guide 2026).

Tier 3 — Knowledge base documents (ground truth). 257 unique candidate questions (after deduplication and normalization) were annotated with categories, types, difficulty levels, answerability flags, and answer hints. Ground truth answer hints are based on authoritative sources covering the 10 thematic categories.

A full analytical review of Indian labor migrant question sources informed the taxonomy. See METHODOLOGY.md (source system) for the complete methodology description.

Genericization and Exclusions

This public release differs from the internal evaluation corpus in the following ways. All modifications are documented here for full transparency.

Excluded Documents (8 files)

Proprietary product documents (6 files) — not included:

6 proprietary third-party product documents (bank / telecom / insurance product sheets) were excluded for licensing reasons and replaced with 6 clearly-labeled synthetic example documents (partner_*.md).

Copyright-restricted documents (2 files) — not included:

• employment_contract_garant.md — content from GARANT legal database (chapter 50.1 of the Russian Labor Code); GARANT reserves copyright on its document corpus
• employment_contract_klerk.md — content from Klerk.ru; source was inaccessible at collection time and the file contains no usable content

Synthetic Replacement Documents (6 files)

Six synthetic documents replace the excluded proprietary product documents. They cover the same functional product categories (migrant debit card, mobile plan, health insurance, deposit for non-residents, salary project for employers, corporate DMS) using fictional brand names ("банк-партнёр", "Партнёр-Мобайл", "Партнёр-Здоровье", etc.). Each synthetic file is clearly marked with a > Synthetic example document notice at the top.

These synthetic documents allow users to test RAG pipelines end-to-end on all product categories without requiring access to the proprietary originals.

Brand Name Genericization in Retained Documents

Brand-specific product and service references in the retained corpus documents were genericized to neutral "partner" wording (e.g. a specific bank card → "partner bank card"). Third-party services unrelated to the genericization were preserved.

Q&A Records: partner_products Category

The four questions in the partner_products category (q099, q100, q101, q111) are retained in the public dataset. These questions test whether a RAG system correctly handles queries about products that are not present in the corpus. They are valid anti-hallucination evaluation probes. Question text and answer_hint fields use neutral "partner bank" wording consistent with the genericized corpus.

Personal and Sensitive Information

The dataset contains no personally identifiable information. Questions are sourced from public Reddit posts (anonymized to question text only, no usernames or post IDs retained), public YouTube comments (text only), and official public websites. No names, contact details, passport numbers, or other personal data appear in any record.

Licensing

This dataset is released under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

You are free to share and adapt the material for any purpose, including commercial use, provided you give appropriate credit:

AdaptaAI Contributors. AdaptaAI: Multilingual RAG Benchmark for Labor Migration Onboarding. 2026. https://huggingface.co/datasets/Qusto/adapta-migrant-onboarding

The corpus documents in corpus/ retain the licenses of their original sources where applicable. Official government documents (migration guides, embassy information) are in the public domain. Documents derived from publicly accessible web content are included under fair use for research and benchmarking purposes. Synthetic documents (partner_*.md) are original works released under CC BY 4.0.

Citation

If you use this dataset in your research, please cite:

@dataset{adaptaai2026,
  title     = {{AdaptaAI}: Multilingual {RAG} Benchmark for Labor Migration Onboarding},
  author    = {AdaptaAI Contributors},
  year      = {2026},
  url       = {https://huggingface.co/datasets/Qusto/adapta-migrant-onboarding},
  license   = {cc-by-4.0},
  note      = {257 Q\&A pairs across English, Russian, Hindi and Hindi-Roman for evaluating RAG systems in the Indian labor migration to Russia domain}
}

Limitations

• Coverage gap: 87% of questions (those not in the partner_products category) were collected before their corresponding corpus documents were available. This means many questions are genuinely unanswerable by the provided corpus and will produce "not in knowledge base" responses in a well-functioning RAG system. This is by design — it measures the system's closed-world behavior.
• Temporal scope: Documents were scraped in May 2026. Legal requirements (biometrics, SIM registration, RuID) and transfer service availability change frequently. Verify key facts before production use.
• Language imbalance: English dominates (225/257 records). Hindi and Russian subsets are small and should be treated as preliminary language parity benchmarks rather than statistically robust test sets.
• Reddit noise: Questions sourced from Reddit include off-topic, loosely related, and politically biased questions (retained deliberately as real-world noise in the community_forum source type). Filter by source_type if you need only high-quality domain questions.
• Proprietary corpus gap: The 6 excluded proprietary product documents created the partner_products anti-hallucination category. Users who add their own bank or product documents to the corpus can convert these from unanswerable to answerable probes.
• RAGAS metrics: The benchmark metrics in benchmark/ragas_metrics.json were computed on the full 35-question internal evaluation subset that included proprietary documents. Scores on the public corpus will differ.

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Краткое описание на русском

Датасет AdaptaAI — многоязычный бенчмарк для оценки RAG-систем в домене адаптации индийских трудовых мигрантов в России.

Состав:

• 257 вопросов на английском, русском, хинди и хинди-романскими буквами
• 27 документов корпуса на русском языке
• RAGAS-метрики из аблационных экспериментов

Категории вопросов: документы и визы, зарплата и контракт, жильё, SIM-карта, денежные переводы, здравоохранение, русский язык, права и безопасность, бытовая жизнь.

Особенность: 20% вопросов помечены answerable: false — они проверяют, правильно ли система отвечает «не знаю», не выдумывая ответ (anti-hallucination тест).

Изменения для публичного релиза: проприетарные документы продуктов третьих сторон исключены и заменены синтетическими аналогами; бренд-специфичные упоминания в оставшихся документах обобщены до нейтральных формулировок («банк-партнёр» и т.п.).

Лицензия: CC BY 4.0.