Update README.md

README.md

@@ -37,4 +37,138 @@ configs:
   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}
+}
+```