Datasets:

Barath
/

genielm-ui-grounding

license: mit
task_categories:
  - text-generation
language:
  - en
tags:
  - ui-grounding
  - gui-automation
  - function-calling
  - agents
  - accessibility
size_categories:
  - 1K<n<10K

GenieLM UI-Grounding

Synthetic supervised fine-tuning data for text-based UI grounding: given a list of on-screen elements (label + pixel center) and a natural-language instruction, pick the single element to act on and emit a strict JSON action.

Built for GenieLM, a macOS agent that reads the accessibility tree as text (not pixels) and lets a small LLM drive the cursor.

Format

Conversational SFT (messages column):

{"messages": [
  {"role": "system", "content": "You are a UI grounding model ... reply with ONLY JSON ..."},
  {"role": "user", "content": "Elements:\n[0] Button \"Go back\" center=(122,40)\n... \n\nInstruction: take me back"},
  {"role": "assistant", "content": "{\"action\":\"click\",\"id\":0}"}
]}

Assistant target schema: {"action":"click"|"type","id":<int>,"text":<string if typing>}, or {"action":"none"} when nothing matches.

Composition

Synthetic element lists across 6 app types: browser, system settings, login forms, code editors, e-commerce, media players.
Direct and paraphrased / indirect instructions ("log in", "take me back", "go fullscreen").
type actions into text/search fields; ~10% no-match negatives.
Reproducible: generated by gen_grounding_dataset.py with a fixed seed.

Intended use

Fine-tune a small decoder (e.g. Qwen3-4B, Gemma) to ground UI instructions to elements + coordinates. A model trained on this drives GenieLM's do/auto agent loop.