arnaiztech/llms-mental-health-crisis-responses

Dataset Card for Between Help and Harm - Responses and Evaluations

Dataset Summary

This dataset repo contains the response-side artifacts prepared for Hugging Face from the paper Between Help and Harm: An Evaluation of Mental Health Crisis Handling by LLMs, published in JMIR Mental Health.

If you use this dataset, please cite the paper. The citation is included below, the arXiv version is available at https://arxiv.org/abs/2509.24857, and the final DOI is allocated as https://doi.org/10.2196/88435.

It is organized around the response-level evaluation task:

• responses/: model responses aligned to canonical example_id
• evaluations/human_raw/: anonymized human appropriateness scores on the validation subset
• evaluations/llm_raw/: raw evaluator-model judgments on model responses
• evaluations/llm_merged/: merged evaluator outputs with per-response aggregate statistics
• evaluations/sampled_review_sets/: sampled low-score review subsets used for agreement analysis

The export preserves the real source JSON structure where possible and adds deterministic response_id and evaluation_id fields so the tables can be joined reliably.

Repository Structure

.
├── README.md
├── responses/
│   ├── validation/
│   └── test/
├── evaluations/
│   ├── human_raw/
│   ├── llm_raw/
│   ├── llm_merged/
│   └── sampled_review_sets/
├── metadata/
│   ├── export_summary.json
│   ├── normalization.json
│   └── provenance.json
└── checksums/
    └── sha256_manifest.json

Included Configurations

• responses: all response tables for validation and test
• human_raw_scores: anonymized human appropriateness scores on the validation subset
• sampled_review_sets: sampled evaluator subsets used for manual comparison
• llm_raw_evaluations_*: raw evaluator runs grouped by evaluator model
• llm_merged_evaluations_*: merged evaluator outputs grouped by evaluator model

Row Counts

• each validation response file: 618 rows
• each test response file: 6,138 rows
• each validation raw evaluator file: 1,854 rows
• each test raw evaluator file under gpt-4o-mini: 18,414 rows
• each merged validation evaluator file: 618 rows
• each merged test evaluator file under gpt-4o-mini: 6,138 rows
• evaluations/human_raw/validation/H1.parquet: 206 rows
• evaluations/human_raw/validation/H2.parquet: 206 rows
• each sampled review file: 206 rows

Schema Notes

Key columns include:

• response_id: deterministic export ID for one model response to one example
• example_id: foreign key to the benchmark repo
• answer_model_raw / answer_model
• evaluator_model_raw / evaluator_model
• response_text
• score, score_runs, score_std
• source_* columns for provenance

Some source JSON rows stored answer payloads or explanations as non-string objects. Those were serialized safely into strings for Parquet compatibility while preserving the original content.

Loading Examples

from datasets import load_dataset

responses = load_dataset("your-username/llms-mental-health-crisis-responses", "responses")
human_scores = load_dataset("your-username/llms-mental-health-crisis-responses", "human_raw_scores")
raw_eval = load_dataset("your-username/llms-mental-health-crisis-responses", "llm_raw_evaluations_gpt_4o_mini")

Intended Uses

This repo is intended for:

• analysis of LLM responses to crisis-related conversations
• appropriateness and agreement studies
• evaluator-model comparison
• reproducing the response-quality analyses in the paper

It is not intended to be used as direct therapeutic advice or as a substitute for clinical review.

Provenance

• exported from the original repository JSON files
• joins validated against inputs and, where needed, answer text
• integrity metadata stored in metadata/provenance.json
• per-file digests stored in checksums/sha256_manifest.json

Citation

@article{arnaiz2026between,
  author = {Arnaiz-Rodriguez, Adrian and Baidal, M. and Derner, E. and Annable, J. L. and Ball, M. and Ince, M. and Perez Vallejos, E. and Oliver, N.},
  title = {Between Help and Harm: An Evaluation of Mental Health Crisis Handling by {LLMs}},
  journal = {JMIR Mental Health},
  year = {2026},
  volume = {forthcoming},
  pages = {88435},
  doi = {10.2196/88435},
  url = {https://arxiv.org/abs/2509.24857},
  note = {In press}
}

License

This dataset is released under the Creative Commons Attribution-NonCommercial 4.0 International license (cc-by-nc-4.0).