Datasets:

jchan58
/

ReLay

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

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:

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

Citation

@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}
}