| --- |
| 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) |
| --- |
| |
| <div style="display: flex; align-items: center; gap: 20px;"> |
| <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. |
|
|
