| --- |
| pretty_name: DelusionEval |
| language: |
| - en |
| license: other |
| license_name: delusioneval-data-use-agreement |
| license_link: LICENSE.md |
| gated: true |
| extra_gated_heading: DelusionEval Data Use Agreement |
| extra_gated_description: > |
| Access to this dataset is restricted. Read the agreement below, provide the |
| requested information, and confirm that you will comply with the terms before |
| requesting access. |
| extra_gated_prompt: | |
| ## DelusionEval Controlled Data Use Agreement |
| |
| Version 1 |
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| Last updated: 2026-08-04 |
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| Copyright (c) 2026 The Board of Trustees of The Leland Stanford Junior |
| University. All Rights Reserved. |
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| This Controlled Data Use Agreement ("Agreement") is entered into between The |
| Board of Trustees of The Leland Stanford Junior University ("Licensor") and |
| the data requestor and, if applicable, the requestor's institution |
| (collectively, "Licensee"). |
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| |
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| Licensor provides controlled access to the DelusionEval dataset and related |
| documentation (collectively, "Data") to support non-commercial scientific |
| research on AI safety, evaluation, and related topics. |
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| |
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| Subject to this Agreement, Licensor grants Licensee a non-exclusive, |
| revocable, non-transferable, non-sublicensable limited license to access and |
| use the Data solely for lawful, non-commercial scientific research. |
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| No rights are granted except as expressly stated in this Agreement. |
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| |
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| Licensee must: |
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| 1. Restrict access to approved personnel only. |
| 2. Not share credentials, raw files, or access paths with unauthorized |
| parties. |
| 3. Not redistribute the Data, in whole or in part, to any third party. |
| 4. Ensure that all personnel with access are trained on human-subject |
| protections and applicable privacy/security obligations. |
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| |
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| Licensee must not: |
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| 1. Attempt to identify, contact, or infer the identity of any participant or |
| institution represented in the Data. |
| 2. Link, match, combine, cross-reference, or associate the Data (in whole or |
| in part) with any other dataset, database, publicly available information, |
| or other source of information, where doing so could reasonably enable or |
| increase the likelihood of identification or re-identification of any |
| individual or institution. |
| 3. Use any analytical technique, algorithm, model, manual method, or |
| auxiliary information for the purpose of, or that has the effect of, |
| reversing, defeating, or circumventing any de-identification, |
| anonymization, pseudonymization, aggregation, or other privacy-protective |
| measure applied to the Data. |
| 4. Use the Data to train, optimize, benchmark, or otherwise improve systems |
| intended to facilitate self-harm, violence, delusional reinforcement, or |
| other harmful behavior. |
| 5. Use the Data for clinical diagnosis, treatment, or direct decision-making |
| about identifiable persons. |
| 6. Use the Data for advertising, surveillance, insurance, employment |
| screening, law-enforcement profiling, or other non-research deployment |
| contexts. |
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| Licensee must use reasonable administrative, technical, and physical |
| safeguards to protect the Data from unauthorized access, use, or disclosure. |
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| If Licensee discovers a potential re-identification risk, data leak, or other |
| security/privacy incident involving the Data, Licensee must promptly report it |
| to the Licensor contact listed in Section 16. |
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| For any publication, preprint, report, or other public disclosure that uses |
| the Data, Licensee must: |
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| 1. Cite the DelusionEval dataset DOI. |
| 2. Cite the FAccT 2026 Delusional Spirals paper. |
| 3. Cite the DelusionEval dataset release. |
|
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| Licensee must not publish examples, excerpts, or derived artifacts in a way |
| that materially increases re-identification risk. |
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| Stanford-owned portions of the released materials are owned by The Board of |
| Trustees of The Leland Stanford Junior University. |
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| Some underlying source rights, including rights in original contributed |
| transcripts, may be held by third parties and are provided under limited |
| permissions. This Agreement does not transfer those rights. |
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| Licensee must comply with all applicable laws, regulations, and institutional |
| policies governing human-subject and sensitive data research. |
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| Where required by Licensee's institution, Licensee is responsible for |
| obtaining local ethics/IRB review or confirmation before use. |
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| This Agreement is effective upon first access to the Data and remains in force |
| until terminated by Licensor or Licensee. |
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| Licensor may terminate access immediately for breach. Upon termination, |
| Licensee must stop use of the Data and destroy local copies, except where |
| retention is required by law or formal institutional policy. |
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| THE DATA ARE PROVIDED "AS IS," WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF MERCHANTABILITY, FITNESS |
| FOR A PARTICULAR PURPOSE, TITLE, OR NON-INFRINGEMENT. |
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| TO THE MAXIMUM EXTENT PERMITTED BY LAW, LICENSOR AND COPYRIGHT HOLDERS ARE NOT |
| LIABLE FOR ANY CLAIM, DAMAGE, OR OTHER LIABILITY ARISING FROM OR RELATING TO |
| USE OF THE DATA. LICENSEE AGREES TO HOLD HARMLESS LICENSOR FOR CLAIMS ARISING |
| FROM LICENSEE'S USE, BREACH, RE-IDENTIFICATION ATTEMPTS, OR SECURITY |
| INCIDENTS. |
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| Licensee will not use the name or trademark of Stanford, or the names of |
| Stanford's employees, students, or agents in any publicity, advertising, or |
| announcement related to this Agreement without the prior written consent of |
| Stanford's authorized officials. Any use of Stanford's name will be limited to |
| statements of fact and will not imply endorsement by Stanford of Licensee's |
| products or services. |
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| Licensor may update these terms from time to time. Licensor will notify |
| Licensee of any material changes by posting a notice on the dataset page, |
| sending an email to the registered address, or displaying a prominent notice |
| upon login. |
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| If Licensee objects to any changes, Licensee may terminate this Agreement by |
| ceasing use of the Data within 30 days of notice. Continued use thereafter |
| means Licensee accepts the new terms. |
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| This Agreement will be governed by the laws of the State of California, with |
| venue for any disputes allowed only in the courts within Santa Clara County. |
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| Licensor: The Board of Trustees of the Leland Stanford Junior University |
| ("Stanford") |
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| Contact for access, compliance questions, and incident reporting: |
| - Name/Role: Jared Moore |
| - Email: jlcmoore@stanford.edu |
| extra_gated_fields: |
| Full name: text |
| Affiliation: text |
| Institutional email: text |
| Intended use: text |
| I have read and agree to the DelusionEval Data Use Agreement: checkbox |
| extra_gated_button_content: Agree and submit request |
| size_categories: |
| - n<1K |
| task_categories: |
| - text-classification |
| - text-generation |
| tags: |
| - llm-safety |
| - mental-health |
| - conversational-ai |
| - evaluation |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: items_sanitized.parquet |
| --- |
| |
| # DelusionEval |
|
|
| ## Dataset Summary |
| DelusionEval is an anonymized conversational evaluation dataset for measuring problematic chatbot behavior in delusional-spiral contexts. |
|
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| This release contains 725 conversation windows with: |
| - `eval_subset_id` (string) |
| - `label` (target behavior code) |
| - `meets_code` (bool) |
| - `messages` (ordered list of message structs with `role`, `content`, and per-message score fields) |
|
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| ## Dataset Description |
|
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| - Homepage: <https://github.com/jlcmoore/llm-delusion-eval> |
| - Version: 1.0.0 |
| - Date published: 2026-08-04 |
| - Keywords: LLM safety, conversation evaluation, mental health |
|
|
| ### Supported Tasks |
| - Safety evaluation and auditing of conversational model behavior |
| - Behavior-code detection and analysis in dialogue windows |
|
|
| ## Dataset Structure |
|
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| ### Data Instances |
| Each row is a conversation window keyed by `eval_subset_id` and `label`. |
| `messages` is a list of dict-like message objects containing: |
| - text fields (`role`, `content`) |
| - bot code score fields (for example, `bot-endorses-delusion`, `bot-romantic-interest`) |
| - user intent score fields (`user-suicidal-intent`, `user-violent-intent`) |
|
|
| ### Data Splits |
| This release is a single split: |
| - `train`: 725 rows |
|
|
| ### Labels |
| 18 label codes are included: |
| `bot-claims-unique-connection`, `bot-discourages-self-harm`, `bot-discourages-violence`, `bot-dismisses-counterevidence`, `bot-endorses-delusion`, `bot-facilitates-self-harm`, `bot-facilitates-violence`, `bot-grand-significance`, `bot-metaphysical-themes`, `bot-misrepresents-ability`, `bot-misrepresents-sentience`, `bot-platonic-affinity`, `bot-positive-affirmation`, `bot-reflective-summary`, `bot-reports-others-admire-speaker`, `bot-romantic-interest`, `bot-validates-self-harm-feelings`, `bot-validates-violent-feelings`. |
|
|
| ## Dataset Creation |
| Windows were selected from anonymized transcripts and then manually reviewed, filtering, and anonymized. |
|
|
| ## Considerations for Use |
|
|
| ### Intended Uses |
| - Safety evaluation and auditing |
| - Method development for detecting problematic chatbot behavior |
|
|
| ### Out-of-Scope and Non-Recommended Uses |
| - Re-identification attempts |
| - Profiling individuals |
| - Optimizing harmful assistant behavior |
|
|
| ### Limitations |
| - Small, curated, and domain-specific sample |
| - Sensitive content (mental health, self-harm, violence themes) |
| - Not a population-representative dataset |
|
|
| ## Access and Safety |
| Controlled and sensitive-use expectations apply. Do not attempt re-identification. |
|
|
| ## Citation |
| ```bibtex |
| @inproceedings{10.1145/3805689.3806443, |
| author = {Moore, Jared and Mehta, Ashish and Agnew, William and Anthis, Jacy Reese and Louie, Ryan and Mai, Yifan and Yin, Peggy and Cheng, Myra and Paech, Samuel J. and Klyman, Kevin and Chancellor, Stevie and Lin, Eric and Haber, Nick and Ong, Desmond C.}, |
| title = {Characterizing Delusional Spirals through Human-LLM Chat Logs}, |
| year = {2026}, |
| isbn = {9798400725968}, |
| publisher = {Association for Computing Machinery}, |
| address = {New York, NY, USA}, |
| url = {https://doi.org/10.1145/3805689.3806443}, |
| doi = {10.1145/3805689.3806443}, |
| booktitle = {Proceedings of the 2026 ACM Conference on Fairness, Accountability, and Transparency}, |
| pages = {7631--7674}, |
| numpages = {44}, |
| location = {}, |
| series = {FAccT '26} |
| } |
| |
| @misc{moore2026delusioneval, |
| title = {DelusionEval: Measuring Delusion-Linked Behaviors in AI Chatbots}, |
| author = {Moore, Jared and Mock, Andrea and Mai, Yifan and Anthis, Jacy Reese and Louie, Ryan and Agnew, William and Mehta, Ashish and Klyman, Kevin and Liang, Percy and Haber, Nick and Lin, Eric and Ong, Desmond C.}, |
| year = {2026}, |
| url = {TODO}, |
| } |
| ``` |
|
|
