README.md

---
dataset_info:
- config_name: image
  features:
  - name: prompt_id
    dtype: string
  - name: prompt
    dtype: string
  - name: caption_instruction
    dtype: string
  - name: category
    dtype: string
  - name: naive
    list:
    - name: image
      dtype: image
    - name: caption
      dtype: string
    - name: sample_idx
      dtype: int32
  - name: persona
    list:
    - name: image
      dtype: image
    - name: caption
      dtype: string
    - name: sample_idx
      dtype: int32
  - name: plan
    list:
    - name: image
      dtype: image
    - name: caption
      dtype: string
    - name: sample_idx
      dtype: int32
  - name: verbalized_k8
    list:
    - name: image
      dtype: image
    - name: caption
      dtype: string
    - name: sample_idx
      dtype: int32
  - name: verbalized_k16
    list:
    - name: image
      dtype: image
    - name: caption
      dtype: string
    - name: sample_idx
      dtype: int32
  - name: gacha
    list:
    - name: image
      dtype: image
    - name: caption
      dtype: string
    - name: sample_idx
      dtype: int32
  splits:
  - name: train
    num_bytes: 36968725345
    num_examples: 50
  download_size: 36972508913
  dataset_size: 36968725345
- config_name: story
  features:
  - name: prompt_id
    dtype: string
  - name: prompt
    dtype: string
  - name: category
    dtype: string
  - name: tier
    dtype: string
  - name: naive
    list: string
  - name: plan
    list: string
  - name: idea
    list: string
  - name: verbalized_k8
    list: string
  - name: verbalized_k16
    list: string
  - name: gacha
    list: string
  splits:
  - name: train
    num_bytes: 123810277
    num_examples: 52
  download_size: 123861146
  dataset_size: 123810277
configs:
- config_name: image
  data_files:
  - split: train
    path: image/train-*
- config_name: story
  data_files:
  - split: train
    path: story/train-*
---

# Qwen3.5-27B diverse-sampling — qualitative examples

Side-by-side qualitative outputs from **`Qwen/Qwen3.5-27B`** under several
prompting methods for diverse sampling, in two domains. One row per prompt; one
column per method holding that method's list of outputs **for the same prompt**,
so a row is a direct method-vs-method comparison.

| config | rows (prompts) | methods | outputs per cell |
|---|---|---|---|
| `story` | 52 | naive, plan, idea, verbalized_k8, verbalized_k16, gacha | up to 128 stories |
| `image` | 50 | naive, persona, plan, verbalized_k8, verbalized_k16, gacha | 128 images + expanded captions |

These are the **complete** n=128 cells — not a benchmark, and not curated. No
filtering, ranking, or cherry-picking: outputs appear in generation order.

Two story cells are ragged rather than exactly 128: `verbalized_k8` (93–128 per
prompt) and `verbalized_k16` (67–128), because the model returns fewer than `k`
items in some list calls. Image `gacha` is missing a single render (6,399 of
6,400).

`persona` is present on `image` but **deliberately absent from `story`** — that
cell was bad and was dropped.

## Schema

`story` and `image` are separate **configs** (their columns are shaped
differently, so they cannot be splits of one dataset):

```python
from datasets import load_dataset
story = load_dataset("scottgeng00/gacha_examples_test", "story")["train"]
image = load_dataset("scottgeng00/gacha_examples_test", "image")["train"]

row = story[0]
row["prompt"]          # 'Write a story titled "It". You decide everything else about it.'
row["gacha"]           # list[str] — the stories
row["naive"][0]        # the same prompt, sampled IID

row = image[0]
row["prompt"]                # original caption the image must depict
row["caption_instruction"]   # what the LLM was asked to expand ("Write a detailed image generation caption for: ...")
row["gacha"][0]["image"]     # PIL.Image, 768x768
row["gacha"][0]["caption"]   # the expanded caption that produced that image
row["gacha"][0]["sample_idx"]
```

Shared columns: `prompt_id`, `prompt`, `category` (+ `tier` on story,
`caption_instruction` on image).

## Provenance

- **Generator:** `Qwen/Qwen3.5-27B`, thinking ON, temperature 1.0, top_p 0.95,
  top_k 20, min_p 0, presence_penalty 1.5.
- **Prompts:** story = 52 open-ended creative-writing prompts; image = 50 OneIG
  captions across 3 categories.
- **Renderer:** `Qwen/Qwen-Image-2512`, 30 steps, `true_cfg_scale` 4.0,
  1328×1328, captions anchored to the original caption. Downscaled to 768×768
  (Lanczos) for this dataset; the 1328² originals are not included.

## Methods

- **naive** — n IID samples of the bare prompt.
- **persona** — a distinct sampled persona (Nemotron-Personas-USA) per sample as
  the system turn.
- **plan** — intent-factored: a plan sampled hot (temperature 1.2), then executed
  at normal temperature.
- **idea** — k=64 numbered "creative angles" in one call, one execute call each.
- **verbalized_k8 / verbalized_k16** — k responses with self-estimated
  probabilities in one call, repeated to cover n.
- **gacha** — per sample, compile a schema of decision points from the task, get
  an options menu per question, roll each choice with a seeded external RNG, then
  render the rolled brief in one fresh call.

## Known quirk: `naive` image captions

Asked to "write a detailed image generation caption for X", `Qwen3.5-27B`
sampled directly answers with a **markdown menu of 2–4 alternative prompts**
("### Option 1: The Cinematic Masterpiece…") in the large majority of cases,
mean ~2,900 characters. That entire blob was passed to the renderer verbatim,
exactly as it was in the run. So `naive` images are conditioned on a
multi-option document rather than a single caption — worth knowing before
reading the `naive` column as a clean IID image baseline. Other methods emit a
single caption (mean 195–1,337 chars).