---
dataset_info:
- config_name: corpus
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 56944692
    num_examples: 300000
  download_size: 31526767
  dataset_size: 56944692
- config_name: counting
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: atoms
    list: string
  splits:
  - name: train
    num_bytes: 5647151
    num_examples: 13020
  download_size: 4695849
  dataset_size: 5647151
- config_name: queries
  features:
  - name: id
    dtype: string
  - name: atom
    dtype: string
  - name: sentence_id
    dtype: string
  splits:
  - name: train
    num_bytes: 3952670
    num_examples: 36337
  download_size: 2137129
  dataset_size: 3952670
- config_name: source_sentences
  features:
  - name: id
    dtype: string
  - name: text
    dtype: string
  - name: n_atoms
    dtype: int64
  splits:
  - name: train
    num_bytes: 2886104
    num_examples: 14483
  download_size: 1749155
  dataset_size: 2886104
configs:
- config_name: corpus
  data_files:
  - split: train
    path: corpus/train-*
- config_name: counting
  data_files:
  - split: train
    path: counting/train-*
- config_name: queries
  data_files:
  - split: train
    path: queries/train-*
- config_name: source_sentences
  data_files:
  - split: train
    path: source_sentences/train-*
license: cc-by-sa-4.0
task_categories:
- text-retrieval
language:
- en
pretty_name: PropSearch
size_categories:
- 100K<n<1M
---

# PropSearch
This benchmark measures the ability of proposition-level embedding models (as well as fixed-size sentence encoders) to retrieve a source sentence starting from an extracted atomic proposition. The benchmark is built on sentences extracted from scientific abstracts ([arXiv abstracts](https://huggingface.co/datasets/gfissore/arxiv-abstracts-2021) and [papers-with-abstracts](https://huggingface.co/datasets/pwc-archive/papers-with-abstracts)).

![immagine](https://cdn-uploads.huggingface.co/production/uploads/6628f9ffcd36b08d03e18ea9/7n2-U1koM-I2fK0C7FXOS.png)

Atomic propositions are extracted by Claude Sonnet 4.6 using the following system prompt:

```
Extract atomic propositions from the given sentence. The propositions need to be independent (for instance, no pronouns that refer to entities outside the proposition itself) and connectives between propositions need to be discarded.

If the sentence only has one atomic proposition, return a singleton.

Return the output as a Python list.
```

In total, the task has 14,483 source sentences, 36,337 query propositions and a corpus of 300,000 confounding sentences.

For the full data creation and validation procedure, check our EMNLP 2026 paper [Variable-size Discovery and Representation of Atomic Propositions](https://aclanthology.org/2026.emnlp-main.2302/).

> [!IMPORTANT]  
> We release this data for evaluation purposes. In the creation of this benchmark, we use Claude Sonnet 4.6 for data generation. Therefore, as to [Anthropic's Privacy and Legal Policies](https://support.claude.com/en/articles/12326764-can-i-use-my-outputs-to-train-an-ai-model), this data **must not** be used to train models that are competitive with Anthropic's own.

## Subsets
The dataset contains four subsets:
* `source_sentences`: The source sentences, along with an identifier and their original number of atomic propositions (`n_atoms`, used to stratify results)
* `queries`: The extracted propositions, each labelled with the correct source sentence ID
* `corpus`: A large number of confounder sentences, with no corresponding atomic propositions
* `counting`: Sentences with their full list of atomic propositions, used to evaluate proposition counting

## Data Distribution

Source Sentences by Atom Count
| Atom Count | Total Sentences |
| :--- | ---: |
| 1 | 2,089 |
| 2 | 5,233 |
| 3 | 3,516 |
| 4 | 2,116|
| 5 | 961 |
| 6+ | 952 |

Query Atoms by Source-sentence Atom Count
| Atom Count | Total Query Atoms |
| :--- | ---: |
| 1 | 1,962 |
| 2 | 9,195 |
| 3 | 8,850 |
| 4 | 7,040 |
| 5 | 3,921 |
| 6+ | 5,369 |

## Citation
If you use this task, please cite us as:

```
@inproceedings{ortame-navigli-2026-variable,
    title = "Variable-size Discovery and Representation of Atomic Propositions",
    author = "Ortame, Francesco  and
      Navigli, Roberto",
    editor = "Lee, Hung-Yi  and
      Dev, Sunipa  and
      Li, Jessy  and
      Elliott, Desmond",
    booktitle = "Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing",
    month = oct,
    year = "2026",
    address = "Budapest, Hungary",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2026.emnlp-main.2302/",
    pages = "52330--52348",
    ISBN = "979-8-89176-459-0"
}
```