---
license: apache-2.0
language:
- it
task_categories:
- question-answering
configs:
- config_name: default
  data_files:
  - split: train
    path: train.jsonl
  - split: italic_sft
    path: italic_sft.jsonl
  - split: italic_sft_ext
    path: italic_sft_ext.jsonl
  - split: quiz_militare
    path: quiz_militare.jsonl
  - split: pinocchio
    path: pinocchio.jsonl
tags:
- italian
- multiple-choice
- knowledge-distillation
- italic
---

# italic-extkd-pool

The stage-3 training data of [idealab-cs2/zagreus-0.4B-italic-extkd](https://huggingface.co/idealab-cs2/zagreus-0.4B-italic-extkd): 57,563 Italian multiple-choice questions from public, in-distribution datasets with teacher soft labels. One soft-KD stage from the stage-2 checkpoint on the agreement-filtered subset (28,561 items where the teacher agrees with the gold answer) reaches **0.4921 / 0.4929 / 0.4932** on the full ITALIC 10K (official harness, 5-shot fast, temperature 0, three independent runs). Full lineage: 0.2802 base -> 0.4787 stage 1 -> 0.4878/0.4880 stage 2 -> this stage.

`train` is the full pool; the other four splits partition it by provenance:

| split | rows | contents |
|---|---|---|
| `train` | 57,563 | the full labeled pool (union of the four below) |
| `italic_sft` | 20,665 | [FinancialSupport/italic_sft](https://huggingface.co/datasets/FinancialSupport/italic_sft): ITALIC-style instruction items from the challenge ecosystem's public data pipeline, 13 category source-splits |
| `italic_sft_ext` | 3,268 | [FinancialSupport/italic_sft_ext](https://huggingface.co/datasets/FinancialSupport/italic_sft_ext): extended ITALIC-style items |
| `quiz_militare` | 3,641 | [FinancialSupport/quiz_militare](https://huggingface.co/datasets/FinancialSupport/quiz_militare): Italian public-exam knowledge MCQs |
| `pinocchio` | 29,989 | [efederici/pinocchio](https://huggingface.co/datasets/efederici/pinocchio) sample: general/culture/law Italian exam questions |

Each row: `question`, `options` (lettered), `answer` (gold letter), `category`, `source` (provenance), `index`, `teacher_logprobs` (soft labels: per-letter log-probabilities from Mistral-Small-3.2-24B-Instruct-2506, public and run locally, prompted in the ITALIC format), `teacher_letter`, `teacher_correct` (teacher/gold agreement flag — the model trained on the `teacher_correct == true` subset of 28,561 items).

Composition note: ~14K questions are shared with the stage-1/stage-2 pools (rehearsal); ~40K are new at this stage, led by the italic_sft/quiz_militare items. The pools are cumulative by design, not independent.

Decontaminated against the ITALIC test set: exact match plus semantic similarity at cosine 0.80 — 0 exact leaks and 0 near-duplicates for italic_sft / italic_sft_ext / quiz_militare; the pinocchio sample showed 0.74% near-duplicates which were dropped (manifest in the submission repo). All splits are answer-position balanced (~25% per letter A–D): position skew in synthetic MCQ pools was found to cause answer-distribution collapse in the student, so balance is verified and documented.

Part of the submission for the [mii-llm Italian Post-Training Challenge](https://huggingface.co/spaces/mii-llm/Post-Training-Challenge).

- Model trained on this data: [idealab-cs2/zagreus-0.4B-italic-extkd](https://huggingface.co/idealab-cs2/zagreus-0.4B-italic-extkd)
- Companion pools: [italic-softkd-pool](https://huggingface.co/datasets/idealab-cs2/italic-softkd-pool) (stage 1) · [italic-m2-culture-pool](https://huggingface.co/datasets/idealab-cs2/italic-m2-culture-pool) (stage 2)
- Training code and report: [github.com/ruggsea/italic-challenge-submission](https://github.com/ruggsea/italic-challenge-submission)

```python
from datasets import load_dataset
ds = load_dataset("idealab-cs2/italic-extkd-pool")               # all five splits
core = load_dataset("idealab-cs2/italic-extkd-pool", split="italic_sft")
train = [r for r in ds["train"] if r["teacher_correct"]]          # the 28,561-item training subset
```

Training, data curation and evaluation by [ruggsea](https://huggingface.co/ruggsea), using compute resources of the Complex Social & Computational Systems (CS²) group, IDea_Lab, University of Graz.

License: Apache 2.0.