Datasets:

Aryanp088
/

StratRAG

	---
	license: apache-2.0
	task_categories:
	- question-answering
	- text-retrieval
	language:
	- en
	tags:
	- rag
	- retrieval-augmented-generation
	- multi-hop-reasoning
	- hotpotqa
	- information-retrieval
	- question-answering
	- evaluation
	size_categories:
	- 1K<n<10K
	---

	# StratRAG

	StratRAG is a retrieval evaluation dataset for benchmarking Retrieval-Augmented Generation (RAG) systems on multi-hop reasoning tasks. It is derived from [HotpotQA](https://hotpotqa.github.io/) (distractor setting) and structured specifically for evaluating retrieval strategies — including sparse (BM25), dense, and hybrid approaches — in realistic, noisy document pool conditions.

	---

	## Why StratRAG?

	Most RAG benchmarks either evaluate end-to-end generation quality or assume clean, small document sets. StratRAG addresses a gap: retrieval evaluation under multi-hop, distractor-heavy conditions, where:

	- Each question requires reasoning across 2 gold documents
	- The retriever must find those 2 docs inside a pool of 15 candidates (13 distractors)
	- Questions span 3 types: bridge, comparison, and yes-no

	This makes it suitable for measuring Recall@k, MRR, NDCG, and faithfulness of retrieved context before any generation step.

	---

	## Dataset Structure

	### Splits

	\| Split \| Rows \|
	\|------------\|------\|
	\| train \| 2000 \|
	\| validation \| 200 \|

	### Schema
	```python
	{
	"id": str, # e.g. "train_000042"
	"query": str, # the multi-hop question
	"reference_answer": str, # ground-truth answer string
	"doc_pool": [ # always exactly 15 documents
	{
	"doc_id": str, # globally unique doc identifier
	"text": str, # title + paragraph body
	"source": str, # paragraph title (from HotpotQA)
	}
	],
	"gold_doc_indices": [int], # indices into doc_pool (always [0, 1])
	# gold docs are always placed first
	"metadata": {
	"split": str, # "train" or "val"
	"question_type": str, # "bridge" \| "comparison" \| "yes-no"
	},
	"created_at": str, # ISO-8601 UTC timestamp
	"provenance": {
	"base": str, # "hotpot_qa(distractor)"
	"seed": int, # 42
	}
	}
	```

	### Key design decisions

	- Gold docs are always at indices 0 and 1 in `doc_pool`. This makes it trivial to compute oracle retrieval metrics and verify your retriever's upper bound.
	- 13 distractor documents per row are drawn from HotpotQA's built-in distractor paragraphs — these are topically related and intentionally difficult to distinguish from gold docs.
	- Empty-text paragraphs are filtered out — HotpotQA contains some paragraphs with no sentence content; these are excluded from distractor slots to ensure all 15 docs have real text.

	---

	## Usage
	```python
	from datasets import load_dataset

	ds = load_dataset("Aryanp088/StratRAG")

	# Inspect a single training example
	row = ds["train"][0]
	print("Query:", row["query"])
	print("Answer:", row["reference_answer"])
	print("Question type:", row["metadata"]["question_type"])
	print("Gold doc indices:", row["gold_doc_indices"])
	print("Number of docs in pool:", len(row["doc_pool"]))

	# Access gold documents directly
	for idx in row["gold_doc_indices"]:
	print(f"\nGold doc [{idx}]:", row["doc_pool"][idx]["text"][:200])
	```

	---

	## Evaluation Example
	```python
	from datasets import load_dataset

	ds = load_dataset("Aryanp088/StratRAG", split="validation")

	def recall_at_k(row, k=2):
	"""
	Simulate a retriever that returns the first k docs (random baseline).
	Replace retrieved_indices with your retriever's output.
	"""
	retrieved_indices = list(range(k)) # replace with your retriever
	gold = set(row["gold_doc_indices"])
	hits = len(gold & set(retrieved_indices))
	return hits / len(gold)

	scores = [recall_at_k(row, k=2) for row in ds]
	print(f"Random Recall@2: {sum(scores)/len(scores):.3f}") # ~0.133 (2/15)
	```

	To benchmark a real retriever (e.g. BM25):
	```python
	from rank_bm25 import BM25Okapi

	def bm25_recall_at_k(row, k=2):
	docs = [doc["text"].split() for doc in row["doc_pool"]]
	query = row["query"].split()
	bm25 = BM25Okapi(docs)
	scores = bm25.get_scores(query)
	top_k = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:k]
	gold = set(row["gold_doc_indices"])
	return len(gold & set(top_k)) / len(gold)

	scores = [bm25_recall_at_k(row, k=2) for row in ds]
	print(f"BM25 Recall@2: {sum(scores)/len(scores):.3f}")
	```

	---

	## Question Type Distribution

	\| Type \| Train \| Validation \|
	\|------------\|-------\|------------\|
	\| bridge \| 1775 \| 171 \|
	\| yes-no \| 113 \| 12 \|
	\| comparison \| 112 \| 17 \|

	---

	## Provenance & Reproducibility

	All rows are derived from HotpotQA (distractor configuration) with `seed=42`.
	```python
	from datasets import load_dataset
	hotpot = load_dataset("hotpot_qa", "distractor")
	```

	---


	---

	## Benchmark Results

	Evaluated on the validation split (n=200) using three retrieval strategies.

	### Overall Results

	\| Retriever \| Recall@1 \| Recall@2 \| Recall@5 \| MRR \| NDCG@5 \|
	\|-----------\|----------\|----------\|----------\|--------\|--------\|
	\| Random \| 0.0525 \| 0.1425 \| 0.3300 \| 0.3190 \| 0.2336 \|
	\| BM25 \| 0.3950 \| 0.6000 \| 0.8150 \| 0.8732 \| 0.7624 \|
	\| Dense (MiniLM-L6-v2) \| 0.4175 \| 0.6500 \| 0.8600 \| 0.9035 \| 0.8087 \|
	\| Hybrid (BM25 + Dense) \| 0.4400 \| 0.6975 \| 0.9050 \| 0.9310 \| 0.8543 \|

	> Hybrid retriever uses equal-weight (α=0.5) min-max normalized score fusion.

	### Hybrid Retriever — By Question Type

	\| Question Type \| n \| Recall@2 \| MRR \| NDCG@5 \|
	\|---------------\|-----\|----------\|--------\|--------\|
	\| bridge \| 171 \| 0.6696 \| 0.9281 \| 0.8418 \|
	\| comparison \| 17 \| 0.8824 \| 0.9706 \| 0.9473 \|
	\| yes-no \| 12 \| 0.8333 \| 0.9167 \| 0.9007 \|

	Key findings:
	- Hybrid retrieval consistently outperforms sparse and dense individually across all metrics
	- Dense retrieval outperforms BM25 on all metrics, highlighting the importance of semantic matching for multi-hop questions
	- Bridge questions are the hardest retrieval type (Recall@2 = 0.67), as they require cross-document reasoning without strong lexical overlap
	- Comparison and yes-no questions benefit more from BM25's keyword matching (higher Recall@2)
	- Recall@5 of 0.905 for Hybrid shows that 90% of the time, both gold documents appear in the top 5 — a strong upper bound for downstream generation

	---

	## Limitations

	- English only — inherited from HotpotQA
	- Wikipedia-domain — all documents are Wikipedia paragraphs; may not generalize to other domains without adaptation
	- 2,200 total rows — suitable for retriever evaluation and fine-tuning signal, not large-scale pretraining
	- Gold position is fixed — gold docs are always at indices 0 and 1. Shuffle `doc_pool` before training retrievers to avoid position bias
	```python
	import random
	random.shuffle(row["doc_pool"]) # shuffle before use in training
	```

	---

	## Citation

	If you use StratRAG in your work, please cite:
	```bibtex
	@dataset{patodiya2026stratrag,
	author = {Patodiya, Aryan},
	title = {StratRAG: A Multi-Hop Retrieval Evaluation Dataset for RAG Systems},
	year = {2026},
	publisher = {Hugging Face},
	url = {https://huggingface.co/datasets/Aryanp088/StratRAG}
	}
	```

	---

	## Author

	Aryan Patodiya — ML Systems Engineer
	MS Computer Science @ California State University, Fresno
	[Portfolio](https://aryanp-portfolio.netlify.app) · [GitHub](https://github.com/aryanpatodiya08) · aryanpatodiya018@gmail.com