Blame for README.md

---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: language
    dtype: string
  - name: source
    dtype: string
  - name: source_id
    dtype: string
  - name: seed_prompt
    dtype: string
  - name: smith_id
    dtype: string
  - name: smith_messages
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  - name: teacher_id
    dtype: string
  - name: teacher_messages
    list:
    - name: role
      dtype: string
    - name: content
      dtype: string
  - name: annotations
    list:
    - name: annotator_id
      dtype: string
    - name: session_id
      dtype: string
    - name: annotated_at
      dtype: timestamp[s]
    - name: duration_ms
      dtype: int64
    - name: preference_naturalness
      dtype: string
    - name: preference_cultural_appropriateness
      dtype: string
    - name: preference_instruction_following
      dtype: string
    - name: preference_coherence
      dtype: string
    - name: preference_overall
      dtype: string
    - name: comment
      dtype: string
    - name: instance_feedback
      dtype: string
  splits:
  - name: train
    num_bytes: 11191230
    num_examples: 2295
  download_size: 10393026
  dataset_size: 11191230
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
language:
- ar
- ceb
- cs
- de
- es
- fr
- id
- ja
- sw
- tl
- yo
license: apache-2.0
task_categories:
- text-generation
tags:
- multilingual
- human-evaluation
- preference
- human-feedback
pretty_name: DataSmith-Preference (Human Evaluation)
---

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  <img alt="Logo for UCam" src="cambridge_logo.png" style="height: 80px; width: auto;">
  <img alt="Logo for LTL" src="ltl_logo2.svg" style="height: 80px; width: auto;">
</div>

**Website:** [ljvmiranda921.github.io/datasmith/](https://ljvmiranda921.github.io/datasmith/)


# DataSmith-Preference-HumanEval

This dataset contains human preference judgments comparing DataSmith against a teacher baseline that generates without tools.
Each comparison shows an evaluator two conversations written from the same English seed prompt, one from each system, and asks which they prefer overall and on four aspects.
Evaluators were native or fluent speakers of the target language, and the two systems were shown as A and B in a randomized order that was hidden from them.
It covers eleven languages: Arabic, Cebuano, Czech, French, German, Indonesian, Japanese, Spanish, Swahili, Tagalog, and Yoruba.

## Dataset Summary

- **Languages:** Arabic (ar), Cebuano (ceb), Czech (cs), French (fr), German (de), Indonesian (id), Japanese (ja), Spanish (es), Swahili (sw), Tagalog (tl), Yoruba (yo)
- **Comparisons:** 2,295
- **Judgments:** 2,317
- **Evaluators:** 24
- **Systems compared:** DataSmith against the teacher baseline
- **Collected:** 7 September 2026 to 25 September 2026
- **Median time per comparison:** 4.2 minutes

### Aspects

Evaluators gave a preference for each aspect below, plus one overall preference.

| Aspect | Description |
|--------|-------------|
| `naturalness` | Sounds natural and human-like, the way a native or fluent speaker would write, without translationese or unnecessary repetition of phrases or ideas. |
| `cultural_appropriateness` | References, names, and scenarios fit the language community and feel contextually appropriate. |
| `instruction_following` | The assistant follows the instructions and requests the user makes throughout the conversation. |
| `coherence` | The conversation stays focused on the topic or transitions logically, without drifting or losing the thread. |

### Preference Values

Evaluators chose between A and B, and we resolved those choices back to the system they refer to. Every `preference_*` field takes one of the following values.

| Value | Meaning |
|-------|---------|
| `strong_smith` | Strong preference for the DataSmith conversation |
| `smith` | Preference for the DataSmith conversation |
| `teacher` | Preference for the teacher conversation |
| `strong_teacher` | Strong preference for the teacher conversation |
| `tie_both_good` | No preference, both conversations judged good |
| `tie_both_bad` | No preference, both conversations judged bad |

### Language Distribution

| Language | Comparisons | Judgments |
|----------|-------------|-----------|
| Arabic (ar) | 232 | 232 |
| Cebuano (ceb) | 230 | 230 |
| Czech (cs) | 168 | 168 |
| French (fr) | 214 | 220 |
| German (de) | 236 | 239 |
| Indonesian (id) | 193 | 193 |
| Japanese (ja) | 210 | 212 |
| Spanish (es) | 196 | 196 |
| Swahili (sw) | 159 | 159 |
| Tagalog (tl) | 229 | 230 |
| Yoruba (yo) | 228 | 238 |
| **Total** | **2,295** | **2,317** |

## Data Sources

The seed prompts are English instructions sampled from the pool used to build the DataSmith training data.

| Source | Comparisons |
|--------|-------------|
| [Nemotron Instruction Following Chat](https://huggingface.co/datasets/nvidia/Nemotron-Instruction-Following-Chat-v1) | 1,554 |
| [Dolci Instruct SFT](https://huggingface.co/datasets/allenai/Dolci-Instruct-SFT) | 741 |

## Dataset Structure

Each row is one comparison and contains the following fields:

| Field | Type | Description |
|-------|------|-------------|
| `id` | str | Identifier for the comparison |
| `language` | str | ISO 639-1 language code, except Cebuano which uses `ceb` |
| `source` | str | Source dataset the seed prompt came from |
| `source_id` | str | Identifier from the source dataset |
| `seed_prompt` | str | The English seed instruction both conversations were written from |
| `smith_id` | str | Identifier of the DataSmith conversation |
| `smith_messages` | list | The DataSmith conversation (`role` and `content`) |
| `teacher_id` | str | Identifier of the teacher conversation |
| `teacher_messages` | list | The teacher conversation (`role` and `content`) |
| `annotations` | list | One entry per evaluator who judged this comparison |

Each entry in `annotations` contains:

| Field | Type | Description |
|-------|------|-------------|
| `annotator_id` | str | Anonymized evaluator identifier |
| `session_id` | str | Annotation session the judgment was made in |
| `annotated_at` | timestamp | When the judgment was submitted |
| `duration_ms` | int | Time spent on the comparison, in milliseconds |
| `preference_naturalness` | str | Preference on naturalness |
| `preference_cultural_appropriateness` | str | Preference on cultural appropriateness |
| `preference_instruction_following` | str | Preference on instruction following |
| `preference_coherence` | str | Preference on coherence |
| `preference_overall` | str | Overall preference |
| `comment` | str | Free-text justification, empty when not given |
| `instance_feedback` | str | Free-text feedback on the comparison itself, empty when not given |


## Usage

```python
from datasets import load_dataset

ds = load_dataset("ljvmiranda921/DataSmith-Preference-HumanEval", split="train")

# Filter by language
arabic_ds = ds.filter(lambda x: x["language"] == "ar")

# Overall preference of the first evaluator on each comparison
print(arabic_ds[0]["annotations"][0]["preference_overall"])

# Win rate for DataSmith, counting strong and plain preferences together
judgments = [a for row in ds for a in row["annotations"]]
wins = sum(1 for a in judgments if a["preference_overall"].endswith("smith"))
print(wins / len(judgments))

# Comparisons with more than one evaluator, for agreement analysis
overlapping = ds.filter(lambda x: len(x["annotations"]) > 1)

# Read what evaluators wrote
for a in judgments:
    if a["comment"]:
        print(a["preference_overall"], a["comment"])
```

## Acknowledgements

We are grateful to the evaluators who participated in our human evaluation for their careful and thoughtful judgments.
LJVM and AK acknowledge the support of the UKRI Frontier Grant EP/Y031350/1 ([EQUATE](https://gtr.ukri.org/projects?ref=EP%2FY031350%2F1)).
Compute was provided by the [Isambard AI National AI Research Resource (AIRR)](https://www.bristol.ac.uk/research/centres/bristol-supercomputing/#isambard-ai) ST/AIRR/I-A-I/1023 and the Microsoft Research Azure AI Grant.
LJVM also thanks the team at Microsoft Research Nairobi and members of the Language Technology Lab for discussions and feedback on this work.