README.md

metadata

language:
  - en
task_categories:
  - text-retrieval
task_ids:
  - document-retrieval
config_names:
  - corpus
tags:
  - text-retrieval
dataset_info:
  - config_name: qrels
    features:
      - name: query-id
        dtype: string
      - name: corpus-id
        dtype: string
      - name: type
        dtype: string
    splits:
      - name: passage
        num_bytes: 598330
        num_examples: 7150
      - name: document
        num_bytes: 485624
        num_examples: 6050
  - config_name: queries
    features:
      - name: _id
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 4781
        num_examples: 76
  - config_name: corpus
    features:
      - name: _id
        dtype: string
      - name: title
        dtype: string
      - name: headings
        dtype: string
      - name: text
        dtype: string
    splits:
      - name: pass_core
        num_bytes: 2712089
        num_examples: 7126
      - name: pass_10k
        num_bytes: 1065541
        num_examples: 2874
      - name: pass_100k
        num_bytes: 33382351
        num_examples: 90000
      - name: pass_1M
        num_bytes: 333466010
        num_examples: 900000
      - name: pass_10M
        num_bytes: 3332841963
        num_examples: 9000000
      - name: pass_100M
        num_bytes: 33331696935
        num_examples: 90000000
      - name: doc_core
        num_bytes: 91711400
        num_examples: 6032
      - name: doc_10k
        num_bytes: 38457420
        num_examples: 3968
      - name: doc_100k
        num_bytes: 883536440
        num_examples: 90000
      - name: doc_1M
        num_bytes: 8850694962
        num_examples: 900000
      - name: doc_10M
        num_bytes: 88689338934
        num_examples: 9000000
configs:
  - config_name: qrels
    data_files:
      - split: passage
        path: qrels/passage.jsonl
      - split: document
        path: qrels/document.jsonl
  - config_name: queries
    data_files:
      - split: test
        path: queries.jsonl
  - config_name: corpus
    data_files:
      - split: pass_core
        path: passage/corpus_core.jsonl
      - split: pass_10k
        path: passage/corpus_10000.jsonl
      - split: pass_100k
        path: passage/corpus_100000.jsonl
      - split: pass_1M
        path: passage/corpus_1000000.jsonl
      - split: pass_10M
        path: passage/corpus_10000000_*.jsonl
      - split: pass_100M
        path: passage/corpus_100000000_*.jsonl
      - split: doc_core
        path: document/corpus_core.jsonl
      - split: doc_10k
        path: document/corpus_10000.jsonl
      - split: doc_100k
        path: document/corpus_100000.jsonl
      - split: doc_1M
        path: document/corpus_1000000.jsonl
      - split: doc_10M
        path: document/corpus_10000000_*.jsonl

CoRE: Controlled Retrieval Evaluation Dataset

Motivation | Dataset Overview | Dataset Construction | Dataset Structure | Qrels Format | Evaluation | Citation | Links | Contact

CoRE (Controlled Retrieval Evaluation) is a benchmark dataset designed for the rigorous evaluation of embedding compression techniques in information retrieval.

🔍 Motivation

Embedding compression is essential for scaling modern retrieval systems, but its effects are often evaluated under overly simplistic conditions. CoRE addresses this by offering a collection of corpora with:

• Multiple document lengths (passage and document) and sizes (10k to 100M)
• Fixed number of relevant and distractor documents per query
• Realistic evaluation grounded in TREC DL human relevance labels

This evaluation framework goes beyond, e.g., the benchmark used in the paper "The Curse of Dense Low-Dimensional Information Retrieval for Large Index Sizes" from Reimers and Gurevych (2021), which disregards different document lengths and employs a less advanced random sampling, hence creating a less realistic experimental setup.

📦 Dataset Overview

CoRE builds on MS MARCO v2 and introduces high-quality distractors using pooled system runs from TREC 2023 Deep Learning Track. We ensure consistent query difficulty across different corpus sizes and document types. This overcomes the limitations of randomly sampled corpora, which can lead to trivial retrieval tasks, as no distractors are present in smaller datasets.

Document Type	# Queries	Corpus Sizes
Passage	65	10k, 100k, 1M, 10M, 100M
Document	55	10k, 100k, 1M, 10M

For each query:

• 10 relevant documents
• 100 high-quality distractors, selected via Reciprocal Rank Fusion (RRF) from top TREC system runs (bottom 20% of runs excluded)

🏗 Dataset Construction

To avoid trivializing the retrieval task when reducing corpus size, CoRE follows the intelligent corpus subsampling strategy proposed by Fröbe et al. (2025). This method is used to mine distractors from pooled ranking lists. These distractors are then included in all corpora of CoRE, ensuring a fixed query difficulty—unlike naive random sampling, where the number of distractors would decrease with corpus size.

Steps for both passage and document retrieval:

1. Start from MS MARCO v2 annotations

2. For each query:

   • Retain 10 relevant documents
   • Mine 100 distractors from RRF-fused rankings of top TREC 2023 DL submissions

3. Construct multiple corpus scales by aggregating relevant documents and distractors with randomly sampled filler documents

🧱 Dataset Structure

The dataset consists of three subsets: queries, qrels, and corpus.

• queries: contains only one split (test)
• qrels: contains two splits: passage and document
• corpus: contains 11 splits, detailed below:

Passage Corpus Splits

Split	# Documents
pass_core	~7,130
pass_10k	~2,870
pass_100k	90,000
pass_1M	900,000
pass_10M	9,000,000
pass_100M	90,000,000

Document Corpus Splits

Split	# Documents
doc_core	~6,030
doc_10k	~3,970
doc_100k	90,000
doc_1M	900,000
doc_10M	9,000,000

Note: The _core splits contain only relevant and distractor documents. All other splits are topped up with randomly sampled documents to reach the target size.

🏷 Qrels Format

The qrels files in CoRE differ from typical IR datasets. Instead of the standard relevance grading (e.g., 0, 1, 2), CoRE uses two distinct labels:

• relevant (10 documents per query)
• distractor (100 documents per query)

This enables focused evaluation of model sensitivity to compression under tightly controlled relevance and distractor distributions.

📊 Evaluation

from datasets import load_dataset

# Load queries
queries = load_dataset("<anonymized>/CoRE", name="queries", split="test")

# Load relevance judgments
qrels = load_dataset("<anonymized>/CoRE", name="qrels", split="passage")

# Load a 100k-scale corpus for passage retrieval
corpus = load_dataset("<anonymized>/CoRE", name="corpus", split="pass_100k")

📜 Citation

If you use CoRE in your research, please cite:

<anonymized>

🔗 Links

• Paper (link is anonymized)
• MS MARCO
• TREC

📬 Contact

For questions or collaboration opportunities, contact <anonymized> at <anonymized>.