Datasets:

gdfwj
/

LifeAgentBench

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.

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

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:

{
  "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:

{
  "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:

{
  "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.

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:

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:

git lfs install
git clone https://github.com/gdfwj/LifeAgentBench.git

For benchmark evaluation scripts and database setup instructions, see the LifeAgentBench repository.

📑 Citation

If you use this dataset, please cite the paper:

@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: