wiki-multihop-qa-500k

500,000 synthetic multi-hop QA pairs generated from Wikipedia. Built for training the think-in-silence latent reasoning model — a model that reasons entirely in vector space without generating chain-of-thought tokens.

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Why This Dataset Exists

Most publicly available QA datasets have two problems for reasoning research:

1. Too small. HotpotQA has 113K samples. StrategyQA has 2.8K. Not enough diversity to train a generalizable reasoning module.
2. Too many single-hop questions. A question answerable from one sentence teaches the model that one thinking step is enough. That directly breaks the K-scaling property we're trying to demonstrate.

This dataset was built to fix both. Every pair requires connecting at least two facts. The model must reason, not look up.

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What Makes a Good Multi-Hop Question

Single-hop (NOT in this dataset):

Q: Where was Marie Curie born?
A: Warsaw

One fact. One lookup. No reasoning chain needed.

Multi-hop (IN this dataset):

Q: In which city did the scientist born in Warsaw later discover polonium?
A: Paris

Two facts connected: (1) Curie was born in Warsaw → (2) she worked in Paris → (3) discovered polonium there.

This is the reasoning pattern that requires K > 1 thinking steps in the think-in-silence architecture.

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Dataset Statistics

Split	Samples
Train	~450,000
Validation	~25,000
Test	~25,000
Total	~500,000

Difficulty Distribution (Train)

Score	Type	Description	Approx %
0	single-hop	One fact lookup	~5%
1	two-hop	Connect two facts	~55%
2	multi-hop	Three or more connections required	~40%

Multi-hop ratio (score ≥ 1): ~95% of the dataset

Answer Type Distribution

Type	Description	Example
entity	Named person, place, or thing	"Paris"
phrase	Short descriptive phrase	"Atlantic Ocean"
numeric	Number or quantity	"1867"
date	Year or date	"1905"
boolean	Yes/No answer	"yes"

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Data Fields

Each sample contains:

{
    "question":         str,   # Multi-hop question requiring 2+ facts
    "answer":           str,   # Short answer (1-6 words)
    "hops":             int,   # Number of reasoning hops (always >= 2)
    "difficulty_score": int,   # 0=single-hop, 1=two-hop, 2=multi-hop
    "answer_type":      str,   # entity | phrase | numeric | date | boolean
    "source":           str,   # Wikipedia chunk file the paragraph came from
}

Example Samples

{
  "question": "In which city did the physicist born in Warsaw conduct her Nobel Prize-winning research?",
  "answer": "Paris",
  "hops": 2,
  "difficulty_score": 2,
  "answer_type": "entity",
  "source": "chunk_0042.jsonl"
}

{
  "question": "What element was discovered by the scientist who also founded the Radium Institute in France?",
  "answer": "Polonium",
  "hops": 2,
  "difficulty_score": 2,
  "answer_type": "entity",
  "source": "chunk_0042.jsonl"
}

{
  "question": "Which country's capital city was home to the university where the Nobel Prize winner who was born in 1867 studied?",
  "answer": "France",
  "hops": 2,
  "difficulty_score": 2,
  "answer_type": "entity",
  "source": "chunk_0089.jsonl"
}

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Generation Pipeline

Source Corpus

• 500,000 English Wikipedia articles streamed from wikimedia/wikipedia (20231101.en)
• Split into 3,308,934 paragraphs (30–200 words each)
• Stored as 331 JSONL chunk files

Generation Model

• Gemini 2.5 Flash-Lite via Google AI API
• 10 concurrent workers
• ~21 pairs/second sustained throughput

Prompt Design

The generation prompt was carefully engineered to force multi-hop output:

STRICT RULES:
1. Each question MUST require connecting 2+ facts from the passage
2. Single-fact questions ("Where was X born?") are REJECTED
3. Answers: 1-6 words maximum
4. Output ONLY a JSON array

GOOD: connects 2 facts
BAD: only 1 fact needed — REJECTED

The good/bad example in the prompt was critical — without it, the model generated mostly single-hop questions despite the instructions.

Quality Filtering

Every generated pair passed through 8 filters before being kept:

Filter	What it removes
single_hop	hops < 2
question_too_short	fewer than 6 words
no_question_mark	missing ?
bad_question_start	doesn't start with question word
answer_too_vague	"yes", "no", "it", "they"
answer_too_long	more than 15 words
answer_in_question	trivial lookup
question_has_artifact	generation artifacts

Plus near-deduplication (Jaccard similarity > 0.85 threshold).

Generation Stats

Raw pairs generated:    1,050,000
After quality filter:   ~925,000   (88%)
After difficulty filter: ~504,000  (48% overall retention)
After deduplication:     ~500,000
Total API cost:          ~$35
Generation time:         ~18 hours

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How to Use

Basic Loading

from datasets import load_dataset

dataset = load_dataset("rajatmalik/wiki-multihop-qa-500k")

# Access splits
train = dataset["train"]
val   = dataset["validation"]
test  = dataset["test"]

print(train[0])
# {
#   "question": "In which city did the physicist born in Warsaw...",
#   "answer": "Paris",
#   "hops": 2,
#   "difficulty_score": 2,
#   "answer_type": "entity",
#   "source": "chunk_0042.jsonl"
# }

Filter by Difficulty

# Only hardest multi-hop (score=2)
hard = dataset["train"].filter(lambda x: x["difficulty_score"] == 2)

# Only two-hop (score=1)
medium = dataset["train"].filter(lambda x: x["difficulty_score"] == 1)

Filter by Answer Type

# Only entity answers
entities = dataset["train"].filter(lambda x: x["answer_type"] == "entity")

# Only numeric
numeric = dataset["train"].filter(lambda x: x["answer_type"] == "numeric")

Combine with Public Datasets

from datasets import load_dataset, concatenate_datasets

wiki_mhop = load_dataset("rajatmalik/wiki-multihop-qa-500k", split="train")
hotpotqa  = load_dataset("hotpot_qa", "distractor", split="train")

# Use together for training
combined = concatenate_datasets([wiki_mhop, hotpotqa])

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Intended Use

Primary Use — think-in-silence Training

This dataset was built specifically to train the ThoughtModule in think-in-silence — a latent reasoning model that performs K recurrent cross-attention steps in a 256-dimensional space.

Multi-hop questions are essential because:

• Single-hop questions teach the model K=1 is enough
• Multi-hop questions force K > 1 thinking steps
• The K-scaling property only emerges with sufficient multi-hop training signal

Other Suitable Uses

• Training retrieval-augmented generation (RAG) systems
• Fine-tuning LLMs for multi-step reasoning
• Evaluating question answering systems
• Research on chain-of-thought and reasoning

Not Suitable For

• Factual question answering benchmarks (answers are synthetic, may contain errors)
• Tasks requiring long-form answers
• Non-English tasks

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Limitations

Answer accuracy is not guaranteed. This is a synthetically generated dataset. Gemini 2.5 Flash-Lite may occasionally generate incorrect answers or misattribute facts. For research purposes, the reasoning structure (multi-hop) matters more than factual accuracy.

Wikipedia coverage. All questions are grounded in Wikipedia (November 2023 snapshot). Topics not well-covered in Wikipedia are underrepresented.

English only. The source corpus is English Wikipedia. All questions and answers are in English.

Retention rate. ~48% of generated pairs passed all filters. The majority of rejections were single-hop questions that slipped through despite the prompt instructions. This means the remaining dataset is high-confidence multi-hop.

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Comparison to Related Datasets

Dataset	Size	Multi-hop	Synthetic	Free
HotpotQA	113K	✓	✗	✓
StrategyQA	2.8K	✓	✗	✓
MuSiQue	20K	✓	✗	✓
wiki-multihop-qa-500k	500K	✓	✓	✓

The key advantage is scale — 500K multi-hop pairs versus the largest public alternative at 113K.

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Generation Code

Full pipeline code is open source:

https://github.com/rajatmalik/think-in-silence-data

To reproduce this dataset:

git clone https://github.com/rajatmalik/think-in-silence-data
cd think-in-silence-data
pip install -r requirements.txt
export GOOGLE_API_KEY=your_key
python run.py --yes

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Citation

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

@dataset{malik2026wikimultihop,
  author    = {Malik, Rajat},
  title     = {wiki-multihop-qa-500k: Synthetic Multi-Hop QA from Wikipedia},
  year      = {2026},
  publisher = {HuggingFace},
  url       = {https://huggingface.co/datasets/rajatmalik/wiki-multihop-qa-500k},
  note      = {Generated using Gemini 2.5 Flash-Lite from 500K Wikipedia articles}
}

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Related Work

• think-in-silence — The model this dataset was built for: github.com/rajatmalik/think-in-silence
• I-JEPA (Assran et al., 2023) — JEPA training objective this project extends to language
• Coconut (Hao et al., 2024) — Related latent reasoning approach
• HotpotQA (Yang et al., 2018) — Original multi-hop QA dataset

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License

Creative Commons Attribution 4.0 (CC BY 4.0)

You are free to use, share, and adapt this dataset for any purpose, including commercial use, as long as you give appropriate credit.

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Built by Rajat Malik · 2026 · Part of the think-in-silence research project