| --- |
| pretty_name: "LifeAgentBench Dataset" |
| license: "cc-by-4.0" |
| language: |
| - en |
| tags: |
| - llm |
| - question-answering |
| - health |
| - reasoning |
| - tabular |
| - text-generation |
| - tool-use |
| size_categories: |
| - 10K<n<100K |
| --- |
| |
| # LifeAgentBench Processed Dataset |
|
|
| The **LifeAgentBench Processed Dataset** is the ready-to-use question-answering collection released with [LifeAgentBench: A Multi-dimensional Benchmark and Agent for Personal Health Assistants in Digital Health](https://arxiv.org/abs/2601.13880). |
|
|
| LifeAgentBench evaluates the ability of large language models (LLMs) to reason over long-horizon, heterogeneous lifestyle and health records. The benchmark contains more than 22K English questions, ranging from direct factual retrieval to aggregation, comparison, consecutive-event reasoning, and trend analysis. |
|
|
| The processed data covers four major lifestyle dimensions: |
|
|
| - **Physical activity**: steps, active minutes, exercise reports, heart rate, and related daily summaries. |
| - **Sleep**: sleep duration, sleep stages, skin temperature, respiratory rate, and oxygen saturation. |
| - **Diet**: meals and food labels. |
| - **Emotion and stress**: emotion-related records and daily stress scores. |
|
|
| The dataset supports: |
|
|
| - **Single-user reasoning** over one participant's longitudinal records. |
| - **Multi-user reasoning** that compares or aggregates information across participants. |
| - **Single-table and multi-table reasoning** within one domain or across multiple lifestyle domains. |
| - **Context prompting** with relevant records embedded directly in the prompt. |
| - **Database-augmented prompting** in which a model generates SQL and answers from the query result. |
|
|
| --- |
|
|
| ## 🗂 Dataset Structure |
|
|
| ```text |
| gen_data_processed/ |
| ├── original/ |
| │ ├── single_user/ |
| │ │ ├── single/ |
| │ │ ├── M-sleep/ |
| │ │ ├── M-activity/ |
| │ │ ├── M-C2/ |
| │ │ └── M-C4/ |
| │ ├── multi_user/ |
| │ │ ├── single/ |
| │ │ └── M-C4/ |
| │ ├── all_prompts.jsonl |
| │ ├── single_user.jsonl |
| │ └── multi_user.jsonl |
| ├── simple/ |
| │ ├── single_user/ |
| │ │ ├── single/ |
| │ │ ├── M-sleep/ |
| │ │ ├── M-activity/ |
| │ │ ├── M-C2/ |
| │ │ └── M-C4/ |
| │ └── all_prompts.jsonl |
| └── sql/ |
| ├── single_user/ |
| │ ├── single/ |
| │ ├── M-sleep/ |
| │ ├── M-activity/ |
| │ ├── M-C2/ |
| │ └── M-C4/ |
| ├── multi_user/ |
| │ ├── single/ |
| │ └── M-C4/ |
| ├── all_prompts.jsonl |
| ├── single_user.jsonl |
| └── multi_user.jsonl |
| ``` |
|
|
| The hierarchy represents the scope and complexity of each task: |
|
|
| - `single_user/single`: single-user, single-table questions. |
| - `single_user/M-sleep`: multi-table reasoning within the sleep domain. |
| - `single_user/M-activity`: multi-table reasoning within the physical-activity domain. |
| - `single_user/M-C2`: cross-domain reasoning involving two lifestyle dimensions. |
| - `single_user/M-C4`: cross-domain reasoning involving all four lifestyle dimensions. |
| - `multi_user/single`: multi-user reasoning over a single table. |
| - `multi_user/M-C4`: multi-user reasoning across all four lifestyle dimensions. |
|
|
| Each leaf dataset directory contains five JSONL files: |
|
|
| - `FQ.jsonl`: factual questions. |
| - `AS.jsonl`: aggregation and statistical questions. |
| - `CQ.jsonl`: counting and consecutive-event reasoning questions. |
| - `NC.jsonl`: numerical comparison questions. |
| - `TA.jsonl`: trend-analysis questions. |
|
|
| The root-level summary files combine subsets for convenient loading: |
|
|
| - `all_prompts.jsonl`: all questions available under the prompting setting. |
| - `single_user.jsonl`: all single-user questions. |
| - `multi_user.jsonl`: all multi-user questions. |
|
|
| The `simple` setting contains only single-user tasks, so it does not include a `multi_user` directory or `multi_user.jsonl`. |
|
|
| --- |
|
|
| ## 📦 Prompting Settings and Data Format |
|
|
| Each line is one JSON object. All answers must follow the output requirements stated in the corresponding question, typically as a semicolon-separated list of values without additional explanation. |
|
|
| ### 1) Original Questions |
|
|
| The `original` directory contains the benchmark questions and ground-truth answers without an added table context or SQL-generation prompt. |
|
|
| - `Query`: natural-language question and output requirements. |
| - `Answer`: ground-truth answer. |
|
|
| Example: |
|
|
| ```json |
| { |
| "Query": "Which user ... achieved the largest increase in steps ...?\nOutput requirement: return 1 value(s); types (ordered): {uid}", |
| "Answer": "A4F_XXXXX" |
| } |
| ``` |
|
|
| ### 2) Context Prompting |
|
|
| The `simple` directory contains questions augmented with compact TSV views of the relevant relational tables. This setting evaluates whether a model can retrieve and reason over evidence supplied directly in its context window. |
|
|
| - `Query`: instructions, the question, output constraints, and compact TSV table context. |
| - `Answer`: ground-truth answer. |
|
|
| Example: |
|
|
| ```json |
| { |
| "Query": "You are given compact TSV views derived from multiple relational tables. ...\n\nQuestion: On the given date, how many steps did the user record?\n\n=== BEGIN TABLE `pa_daily_summary` (compact TSV, id scoped) ===\nid\tdate\tsteps\nA4F_XXXXX\t2022-05-30\t10019\n=== END TABLE ===", |
| "Answer": "10019" |
| } |
| ``` |
|
|
| ### 3) Database-Augmented Prompting |
|
|
| The `sql` directory separates SQL generation from final answer generation. The model first produces a MySQL `SELECT` statement, the query is executed against the LifeAgentBench database, and the returned result is then used to answer the original question. |
|
|
| - `Query_sql`: question plus MySQL schema and SQL-generation instructions. |
| - `Query_base`: prompt template for generating the final answer from the SQL statement and execution result. |
| - `Answer`: ground-truth answer. |
|
|
| Example: |
|
|
| ```json |
| { |
| "Query_sql": "Given the following MySQL table schema, write ONE SELECT statement ...", |
| "Query_base": "Answer the question using the executed SQL and returned result ...", |
| "Answer": "A4F_XXXXX" |
| } |
| ``` |
|
|
| --- |
|
|
| ## 🚀 Usage Examples |
|
|
| Install the Hugging Face `datasets` library and load the desired JSONL file directly. |
|
|
| ```python |
| from datasets import load_dataset |
| |
| # Original benchmark questions |
| original = load_dataset( |
| "json", |
| data_files="gen_data_processed/original/all_prompts.jsonl", |
| split="train", |
| ) |
| |
| # Context-prompting questions |
| context = load_dataset( |
| "json", |
| data_files="gen_data_processed/simple/all_prompts.jsonl", |
| split="train", |
| ) |
| |
| # Database-augmented questions |
| sql = load_dataset( |
| "json", |
| data_files="gen_data_processed/sql/all_prompts.jsonl", |
| split="train", |
| ) |
| ``` |
|
|
| To load files from the hosted dataset repository: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| dataset = load_dataset( |
| "json", |
| data_files="hf://datasets/gdfwj/LifeAgentBench/gen_data_processed/original/all_prompts.jsonl", |
| split="train", |
| ) |
| ``` |
|
|
| The repository uses Git LFS for large files. When cloning the full dataset, install Git LFS first: |
|
|
| ```bash |
| git lfs install |
| git clone https://github.com/gdfwj/LifeAgentBench.git |
| ``` |
|
|
| For benchmark evaluation scripts and database setup instructions, see the [LifeAgentBench repository](https://github.com/gdfwj/LifeAgentBench). |
|
|
| --- |
|
|
| ## 📑 Citation |
|
|
| If you use this dataset, please cite the paper: |
|
|
| ```bibtex |
| @article{tian2026lifeagentbench, |
| title={LifeAgentBench: A Multi-dimensional Benchmark and Agent for Personal Health Assistants in Digital Health}, |
| author={Tian, Ye and Wang, Zihao and Gungor, Onat and Fan, Xiaoran and Rosing, Tajana}, |
| journal={arXiv preprint arXiv:2601.13880}, |
| year={2026} |
| } |
| ``` |
|
|
| --- |
|
|
| ## ⚠️ Notes |
|
|
| - LifeAgentBench is intended for research and benchmarking; it is not a medical device and must not be used as a substitute for professional diagnosis or treatment. |
| - Participant identifiers are pseudonymous. Users should nevertheless handle all lifestyle and health-related records responsibly and follow applicable privacy and ethical requirements. |
| - Questions may require exact formatting. Follow the `Output requirement` included in each prompt when evaluating model predictions. |
| - The three top-level directories are alternative representations of related benchmark questions, not independent train, validation, and test splits. |
|
|
| ## ⚠️ Licensing & Compliance |
|
|
| The dataset card declares the processed benchmark under the **Creative Commons Attribution 4.0 International (CC BY 4.0)** license. The benchmark is derived from underlying lifestyle and food data sources; users are responsible for reviewing and complying with the licenses, terms of use, privacy requirements, and citation requirements of those original sources. |
|
|
| The database construction workflow uses: |
|
|
| - [AI4FoodDB](https://github.com/AI4Food/AI4FoodDB) |
| - [FoodNExtDB](https://bidalab.eps.uam.es/static/AI4FoodDB/FoodNExtDB.zip) |
|
|
|
|
