--- pretty_name: DECKEDIT-BENCH license: cc-by-nc-4.0 language: - en - ko tags: - powerpoint - pptx - instruction-following - document-editing - agents - benchmark size_categories: - n<1K task_categories: - other configs: - config_name: prompts data_files: prompts.jsonl --- # DECKEDIT-BENCH A benchmark for **instruction-guided PowerPoint (`.pptx`) editing**. It pairs 28 real-world presentation decks with **183 natural-language editing instructions**, organized along a **Cell × Action × Target** taxonomy and three deck-length tiers (Short / Medium / Long). Each instance is a `(deck, instruction)` pair: a system reads the instruction, edits the deck, and the before/after decks are compared. The benchmark stresses locating the right targets, decomposing multi-step instructions, compressing repeated edits into a pattern, and **not damaging what should stay untouched**. ## Contents ``` DECKEDIT-BENCH/ ├── decks/{Short,Medium,Long}/*.pptx # 28 source decks (≤10 / 11–30 / >30 slides) ├── prompts.jsonl # 183 editing instructions (one JSON / line) ├── taxonomy.json # cell + action definitions, 14 reusable templates ├── deck_info.json # per-deck metadata (size, language, topic, visuals) ├── DATASHEET.md # datasheet-for-datasets (composition, collection, cleaning) ├── TAXONOMY.md # Cell × Action × Target taxonomy + template catalog ├── THIRD_PARTY_NOTICES.md # image/figure provenance & attributions ├── LICENSE # data license (CC BY-NC 4.0) └── CITATION.cff ``` ## Prompt format (`prompts.jsonl`) One JSON object per line: ```json {"deck_id":"02_UTI","id":"02_UTI-01","cell":"PS","ops_per_slide":1, "actions":["REPLACE"],"derived_from":"U-01", "intent":"…what this instance tests…","text":"…instruction sent to the system…"} ``` - `text` — the instruction handed to the editing system. - `cell` ∈ `{ES, EC, PS, PC}` · `actions` ⊆ `{ADD, DELETE, REPLACE, SLIDE}`. - `derived_from` — a universal template id (`U-xx`, see `taxonomy.json`) or `"deck-specific"`. See **TAXONOMY.md** for definitions. ## Quickstart ```python import json prompts = [json.loads(l) for l in open("prompts.jsonl", encoding="utf-8")] info = {d["id"]: d for d in json.load(open("deck_info.json", encoding="utf-8"))["decks"]} p = prompts[0] deck_path = info[p["deck_id"]]["path"] # e.g. "decks/Short/01_CausalAGI.pptx" # 1. open deck_path, 2. apply p["text"] with your system, 3. diff before/after. ``` ## Coverage | | total | breakdown | |---|---|---| | Decks | **28** | Short 9 · Medium 12 · Long 7 | | Prompts | **183** | Short 54 · Medium 81 · Long 48 | | Cells | — | ES 17 · EC 57 · PS 87 · PC 22 | | Deck languages | — | en 21 · ko 7 (translate targets also span ja/fr/es/zh) | Full Cell × Action matrix and known imbalances are in **DATASHEET.md**. ## Notes on the decks - **Full resolution.** The `.pptx` files keep original-resolution media (~1 GB total) and are distributed via Git LFS / the dataset host. - **Privacy.** Every deck was scrubbed of author/coauthoring/comment PII; see **DATASHEET.md → Preprocessing/cleaning**. - **Provenance.** Embedded figures in paper-review decks, and the replacement images in `11_FourthIndustryStartup`, are credited in **THIRD_PARTY_NOTICES.md**. ## License & citation Data is released under **CC BY-NC 4.0** (see `LICENSE`). If you use DECKEDIT-BENCH, please cite it — see `CITATION.cff`.