--- 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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**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.