---
license: cc-by-4.0
dataset_info:
  features:
  - name: id
    dtype: string
  - name: base_prompt
    dtype: string
  - name: animal1
    dtype: string
  - name: animal2
    dtype: string
  - name: action
    dtype: string
  - name: rarity_label
    dtype: string
  - name: emotional_valence
    dtype: string
  - name: spatial_topology
    dtype: string
  - name: temporal_extent
    dtype: string
  - name: emotional_prompt
    dtype: string
  - name: spatial_prompt
    dtype: string
  - name: temporal_prompt
    dtype: string
  splits:
  - name: train
    num_bytes: 94793
    num_examples: 125
  download_size: 51438
  dataset_size: 94793
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
task_categories:
- text-to-image
language:
- en
tags:
- T2I
- Reasoning
- Action
- Benchmark
size_categories:
- n<1K
---
# AcT2I-Prompts

## What is this?

AcT2I-Prompts is the core prompt set from the AcT2I benchmark. It contains 125 base action-centric prompts that describe interactions between two animal agents (e.g. "a goose competing for dominance with a turkey") plus 3 enriched variants per base prompt:

* `spatial_prompt`
* `emotional_prompt`
* `temporal_prompt`

Each base prompt is also labeled along semantic axes:

* `rarity_label` (how biologically/common the interaction is)
* `emotional_valence` (aggressive / defensive / affiliative / communicative)
* `spatial_topology` (pursuit vs physical contact vs distant interaction)
* `temporal_extent` (instantaneous vs extended action)

Total rows: 125 (one per base prompt). Each row includes all 4 textual variants.

This repo intentionally does **not** include generated images or human study results. Those are released separately.

---

## Intended use

This dataset is **for evaluation / analysis of text-to-image models**, not for training.

Typical use:

1. For each row, take the `base_prompt` and (optionally) the enriched `spatial_prompt`, `emotional_prompt`, and `temporal_prompt`.
2. Generate images from your T2I model for each variant.
3. Measure whether the model's image actually depicts the described interaction and action.

These prompts are meant to stress-test spatial, temporal, and affective reasoning ("who is doing what to whom, in what posture, with what intent, at what moment").

### Out-of-scope / disallowed use

This dataset is **not** intended for:

* Training or promoting violent / graphic animal content for shock or harassment.
* Generating deceptive media presented as "real" wildlife attacks or staged cruelty.
* Drawing conclusions about human social behavior, human interpersonal violence, or human identity bias. The benchmark is deliberately animal–animal and two-agent focused.

Do not use this dataset to build abusive content pipelines.

---

## Data fields

Each row in `data/prompts.jsonl` represents one base interaction scenario.

* `id` (int)
* `base_prompt` (str)
* `animal1` (str)
* `animal2` (str)
* `action` (str)
* `rarity_label` (str: `frequent` | `rare` | `very_rare`)
* `emotional_valence` (str: `aggressive` | `defensive` | `affiliative` | `communicative`)
* `spatial_topology` (str: `proximal-contact` | `pursuit / avoidance` | `distant interaction`)
* `temporal_extent` (str: `instantaneous` | `extended action`)
* `spatial_prompt` (str)
* `emotional_prompt` (str)
* `temporal_prompt` (str)

There are no train/dev/test splits. All 125 rows are considered the official evaluation set.

---

## Dataset creation

### Curation rationale

Most existing "compositional" prompts test simple attribute binding ("a blue cat on a skateboard"). AcT2I instead targets **interaction semantics**: chasing, comforting, retaliating, asserting dominance, surrendering, etc. These require:

* asymmetric roles (one agent acts on the other),
* physically plausible contact / pursuit / restraint poses,
* temporal cues (in the middle of an attack vs after being struck),
* emotional / intent cues (aggressive vs affiliative).

We focus on animal–animal interactions (instead of human–human violence or human identity scenarios) to:

1. Reduce sensitive social/ethical risk around representing harm between humans.
2. Get clearer signal about action depiction instead of immediately running into "the model can't draw human hands" failures.

### How prompts were generated

* We defined pairs of animals and an interaction verb (e.g. "competing for dominance with", "comforting", "chasing", "retaliating against").
* We wrote a concise `base_prompt` for each interaction.
* For each base prompt, we produced three enriched variants:

  * `spatial_prompt`: adds explicit body orientation / physical layout.
  * `emotional_prompt`: adds affect / intent wording.
  * `temporal_prompt`: anchors the scene in a specific moment or phase of action.
* We assigned semantic labels (`rarity_label`, `emotional_valence`, `spatial_topology`, `temporal_extent`) to each base prompt.

### Who created the data

All prompts, enriched variants, and semantic labels were authored/verified by the AcT2I team. No personal names, locations, or other PII were included.

---

## Bias, risks, and limitations

* **Violence / aggression content:** Many prompts explicitly describe aggression, dominance, pursuit, or threat between animals. This is intentional (models struggle most with these high-contact, asymmetric actions). However, it means the dataset can be used to generate violent-looking content. Please use responsibly.

* **Scope limitations:** The benchmark is animal–animal only and two-agent only. Results should not be overgeneralized to human social interactions, medical scenarios, multi-agent scenes, tool use, etc.

* **Biological plausibility:** Some interactions are biologically rare or borderline impossible. That is deliberate: we care about whether the model can depict the *requested* interaction clearly, not whether the interaction is common in nature.

---

## Citation

If you use AcT2I-Prompts, please cite:

```bibtex
@article{malaviya2025act2i,
  title={AcT2I: Evaluating and Improving Action Depiction in Text-to-Image Models},
  author={Malaviya, Vatsal and Chatterjee, Agneet and Patel, Maitreya and Yang, Yezhou and Baral, Chitta},
  journal={arXiv preprint arXiv:2509.16141},
  year={2025}
}
```