---
dataset_info:
  features:
  - name: doc
    dtype: string
  - name: split
    dtype: string
  - name: domain
    dtype: string
  splits:
  - name: train
    num_bytes: 26636395
    num_examples: 395892
  - name: test
    num_bytes: 8601871
    num_examples: 114099
  download_size: 5977728
  dataset_size: 35238266
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
license: cc-by-nc-sa-4.0
pretty_name: hyperprobe-dataset
task_categories:
- feature-extraction
language:
- en
tags:
- decoding
- probing
- llms
- concepts
- analogy
size_categories:
- 100K<n<1M
---

# Dataset Card for *hyperdimensional probe*

This repository contains the official datasets of "[*Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures*](https://arxiv.org/abs/2509.25045)".
- This work combines symbolic representations and neural probing to introduce Hyperdimensional Probe, a new paradigm for decoding LLM vector space into human-interpretable features, consistently extracting meaningful concepts across models and inputs.

# Datasets
1. **Corpus of factual and linguistic analogies (input-completition tasks):** [saturnMars/hyperprobe-dataset-analogy](https://huggingface.co/datasets/saturnMars/hyperprobe-dataset-analogy)
2. SQuAD-based corpus (question-answering tasks): [saturnMars/hyperprobe-dataset-squad](https://huggingface.co/datasets/saturnMars/hyperprobe-dataset-squad)

## Dataset Details

### Dataset Description
This repository includes our syntethic corpora for the [training](https://huggingface.co/datasets/saturnMars/hyperprobe-dataset-analogy/blob/main/data/train-00000-of-00001.parquet) and [experimental](https://huggingface.co/datasets/saturnMars/hyperprobe-dataset-analogy/blob/main/data/test-00000-of-00001.parquet) stages.

### Information
- **Curated by:** Marco Bronzini
- **Funded by:** [IpaziaAi](https://github.com/Ipazia-AI)
- **Language(s) (NLP):** English
- **License:**  [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en) (CC BY-NC-SA 4.0).

### Dataset Sources
- **Repository:** [github.com/Ipazia-AI/hyperprobe](https://github.com/Ipazia-AI/hyperprobe)
- **Paper:** [Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures](https://arxiv.org/abs/2509.25045)

## Examples
### Train data
```json
[
  " 10 : 1 = 60 : 6",
  " 10 : 100 = 12 : 144",
  " plato : kepler = philosopher : mathematician",
  " significant : successful = significantly : successfully",
  " important : importantly = subsequent : subsequently"],
  " 10 : 100 = 28 : 784",
  " coyote : canine = cat : feline",
  " coyote : canine = cow : bovid",
  " sold : oversold = played : overplayed",
  " sold : oversold = populated : overpopulated"],
  " 10 : 1 = 80 : 8",
  " rarely : quietly = rare : quiet",
  " rarely : rare = calmly : calm",
  " rarely : rare = critically : critical",
  " youngest : young = sweetest : sweet"]
]
```

### Test data
```json
{
    "capital_world": [
        " Athens is to Greece as Baghdad is to Iraq",
        " Athens is to Greece as Bangkok is to Thailand"],
    "currency": [
        " Algeria is to dinar as Angola is to kwanza",
        " Algeria is to dinar as Brazil is to real"],
    "family": [
        " boy is to girl as brother is to sister",
        " boy is to girl as dad is to mom"],
    "comparative": [
        " bad is to worse as big is to bigger",
        " bad is to worse as bright is to brighter"],
    "verb_Ving_3pSg": [
        " adding is to adds as advertising is to advertises",
        " adding is to adds as appearing is to appears"],
    "male_female": [
        " actor is to actress as batman is to batwoman",
        " actor is to actress as boy is to girl"]
}
```

## Source Data

This corpora were generated using two knowledge bases:
1. [Google Analogy Test Set](https://aclweb.org/aclwiki/Google_analogy_test_set_(State_of_the_art));
2. [The Bigger Analogy Test Set (BATS)](https://vecto.space/projects/BATS).

Google Analogy Test Set is distributed by TensorFlow under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0.txt), whereas BATS is released under the [CC-BY-NC 4.0 License](https://creativecommons.org/licenses/by-nc/4.0/deed.en).
- See the [``GitHub repository``](https://github.com/Ipazia-AI/hyperprobe/blob/main/src/hyperprobe/data_creation/create_texts.py) to reconstruct the corpora from the these two knowledge bases.

## Citation
If you use any of these datasets in your research, please cite the following work:

```bibtex
@misc{bronzini2025hyperdimensional,
    title={Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures},
    author={Marco Bronzini and Carlo Nicolini and Bruno Lepri and Jacopo Staiano and Andrea Passerini},
    year={2025},
    eprint={2509.25045},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```

**APA:**
Bronzini, M., Nicolini, C., Lepri, B., Staiano, J., & Passerini, A. (2025). Hyperdimensional Probe: Decoding LLM Representations via Vector Symbolic Architectures.