Datasets:
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configs:
- config_name: personalized
data_files: "personalized/pls_results.json"
- config_name: nonpersonalized
data_files: "nonpersonalized/nonpersonalized_results.json"
- config_name: participant_surveys
data_files: "participant_surveys/participant_surveys.json"
- config_name: all_surveys
data_files: "all_surveys/health_surveys.json"
license: mit
task_categories:
- text-generation
- summarization
language:
- en
size_categories:
- 10K<n<100K
tags:
- health
- plain-language-summaries
- personalization
- evaluation
---
# ReLay: Personalized LLM-Generated Plain-Language Summaries
Experiment data for the paper: *ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding of Health Information, but at What Cost?*
## Configurations
| Config | Records | Description |
|--------|---------|-------------|
| `personalized` | 11,580 | Personalized PLS from 5 models x 6 methods, with style alignment and bias judgments |
| `nonpersonalized` | 1,530 | Non-personalized baseline PLS from 5 models, with knowledge alignment and hallucination evaluations |
| `participant_surveys` | 50 | Survey responses for the 50 study participants (IDs 1-50) |
| `all_surveys` | 128 | Full survey pool including all respondents (IDs 1-128) |
## Usage
```python
from datasets import load_dataset
# Load a specific config
personalized = load_dataset("jchan58/ReLay", "personalized")
nonpersonalized = load_dataset("jchan58/ReLay", "nonpersonalized")
participant_surveys = load_dataset("jchan58/ReLay", "participant_surveys")
all_surveys = load_dataset("jchan58/ReLay", "all_surveys")
```
Or download files directly:
```python
from huggingface_hub import hf_hub_download
hf_hub_download(repo_id="jchan58/ReLay", filename="personalized/pls_results.json", repo_type="dataset", local_dir=".")
```
## Key Fields
### Personalized (11,580 records)
- `prolific_id` — anonymized participant ID (integer 1-50)
- `batch` — study phase (static1, static2, interactive3)
- `abstract_id` — Cochrane abstract identifier
- `model` — generation model (gpt-4o, gpt-5.2, Mistral-7B, MedGemma-27B, Qwen3-4B)
- `method` — personalization method (backstory, metadata, within-user RAG, cross-user RAG, etc.)
- `generated_pls` — the generated plain-language summary
- `abstract` / `human_pls` — source abstract and human-written PLS
- `style_alignment` — style alignment score (0-1)
- `bias_judgment` — LLM judge output for bias-reinforcing framing
### Nonpersonalized (1,530 records)
Same core fields plus:
- `knowledge_result` — LLM judge output for knowledge alignment
- `hallucination_eval` — claim-level faithfulness and factuality scores
### Participant Surveys (50 study participants)
- `participant_id` — anonymized ID (1-50, maps to `prolific_id` in PLS results)
- Health topic familiarity and interest ratings
- AI tool usage patterns
- Health literacy indicators
- Information-seeking behavior
### All Surveys (128 respondents)
Same schema as participant surveys. IDs 1-50 are the study participants; IDs 51-128 are additional survey respondents who did not complete the full study.
## Code
See the full codebase: [github.com/jchan58/ReLay](https://github.com/jchan58/ReLay)
## Citation
```bibtex
@article{chan2026relay,
title={ReLay: Personalized LLM-Generated Plain-Language Summaries for Better Understanding of Health Information, but at What Cost?},
author={Chan, Joey and Han, Yikun and Chen, Jingyuan and Fang, Samuel and Gryboski, Lauren D and Lee, Alexandra and Tanna, Sheel and Zhu, Qingqing and Lu, Zhiyong and Wang, Lucy Lu and others},
journal={arXiv preprint arXiv:2605.00468},
year={2026}
}
```
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