---
license: other
task_categories:
- automatic-speech-recognition
language:
- en
tags:
- speech
- speech-language-models
- spoken-language-understanding
- common-sense
- factual-knowledge
- factual-recall
- likelihood-evaluation
- audio-reasoning
- interpretability
configs:
- config_name: default
  data_files:
  - split: data
    path: data/data-*
dataset_info:
  features:
  - name: id
    dtype: string
  - name: category
    dtype: string
  - name: prompt_text
    dtype: string
  - name: fact_text
    dtype: string
  - name: counterfactual_text
    dtype: string
  - name: answer
    dtype: string
  - name: counterfactual_answer
    dtype: string
  - name: prompt_audio
    dtype: audio
  - name: fact_audio
    dtype: audio
  - name: counterfactual_audio
    dtype: audio
  - name: prompt_words
    sequence: string
  - name: prompt_word_starts
    sequence: float32
  - name: prompt_word_ends
    sequence: float32
  splits:
  - name: data
    num_bytes: 63595363.0
    num_examples: 281
  download_size: 52788867
  dataset_size: 63595363.0
---

# Common-Sense Facts Audio Dataset

A spoken fact-completion dataset for evaluating whether Speech Language Models can retrieve common-sense and factual knowledge from speech.

Each example contains three paired versions:

1. **prompt**: an incomplete factual prompt, e.g. *"the capital of France is"*
2. **fact**: the correct full sentence, e.g. *"the capital of France is Paris"*
3. **counterfactual**: an incorrect matched sentence from the same category, e.g. *"the capital of France is Rome"*

The dataset covers categories such as capitals, colors, arithmetic, opposites, family relations, object functions, and common-sense facts.

It is designed for likelihood-based evaluation: a Speech LM succeeds when it assigns higher likelihood to the correct fact than to the matched counterfactual. The dataset also includes prompt-level audio and word-level timing information, making it useful for interpretability analyses of how speech-derived representations encode lexical and factual information.

Introduced in:

**[Interleaved Speech Language Models Latently Work In Text](https://arxiv.org/abs/2606.22473)**

📃 [Paper](https://huggingface.co/papers/2606.22473) | 🌐 [Project Page](https://pages.cs.huji.ac.il/adiyoss-lab/slm_work_in_text/)



## Usage

```python
from datasets import load_dataset

ds = load_dataset("slprl/common-sense-facts-audio", split="data")

# Access a single example
row = ds[0]
print(row["id"])            # "colors_001"
print(row["category"])      # "Colors"
print(row["prompt_text"])   # "The color of grass is"
print(row["answer"])        # "green"

# Audio is a dict with array and sampling_rate
audio = row["prompt_audio"]   # {"array": np.ndarray, "sampling_rate": int}

# Filter by category
capitals = ds.filter(lambda x: x["category"] == "Capital cities")
```

---

## Columns

| Column | Type | Description |
|---|---|---|
| `id` | string | Clean example ID, e.g. `capital_cities_001` |
| `category` | string | Semantic category |
| `prompt_text` | string | Incomplete factual prompt |
| `fact_text` | string | Correct full sentence |
| `counterfactual_text` | string | Incorrect full sentence |
| `answer` | string | Correct answer word/phrase |
| `counterfactual_answer` | string | Incorrect answer word/phrase |
| `prompt_audio` | Audio | WAV of the prompt |
| `fact_audio` | Audio | WAV of the correct full sentence |
| `counterfactual_audio` | Audio | WAV of the incorrect full sentence |
| `prompt_words` | list[str] | Word tokens of the prompt |
| `prompt_word_starts` | list[float] | Word start times in seconds |
| `prompt_word_ends` | list[float] | Word end times in seconds (-1.0 = last word; use audio duration) |

---

## Examples

| id | category | prompt_text | answer | counterfactual_answer |
|---|---|---|---|---|
| colors_001 | Colors | The color of grass is | green | red |
| days_001 | Days of the week | The day that comes after Sunday is | Monday | Friday |
| capital_cities_001 | Capital cities | The capital of France is | Paris | New Delhi |
| opposites_001 | Opposites | the opposite of hot is | cold | slow |
| family_relations_001 | Family relations | The mother of mother is called | grandmother | grandfather |
| simple_arithmetic_001 | Simple arithmetic | one plus one equals | 2 | 0 |

---

## Citation

```bibtex
@misc{sternberg2026interleavedspeechlanguagemodels,
      title={Interleaved Speech Language Models Latently Work In Text}, 
      author={Talia Sternberg and Gallil Maimon and Yossi Adi},
      year={2026},
      eprint={2606.22473},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2606.22473}, 
}
```