Uses WildChat
Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild
COLM 2024 · First arXiv submission
Studies the tasks and contexts in which people disclose personal or sensitive information to chatbots. It combines automated PII detection with task and sensitive-topic taxonomies, releasing annotations that support analyses beyond conventional identifiers.
Dataset and scope
A 5,000-conversation English sample from the one-million-conversation WildChat corpus, limited to one conversation per hashed IP with additional prefix and length filters.
Selected finding
In the analyzed sample, sensitive disclosures extend beyond the categories recognized by PII detectors, including preferences, personal habits, and application-related information.
Section 2 specifies sampling; Sections 3–4 compare detected identifiers with sensitive topics. The observations apply to this filtered sample. Read the source.
Dates and versions
First arXiv version. ArXiv version history.
Cites the arXiv preprint with its original 2024 preprint year. The paper also appeared at COLM 2024.
The annotation describes the linked source version. Consult the paper for its full methods, assumptions, and limitations.
Cite this paper
Niloofar Mireshghallah, Maria Antoniak, Yash More, Yejin Choi, Golnoosh Farnadi. 2024. Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild. arXiv preprint.
Show BibTeX
@misc{mireshghallah2024trustnobot,
title = {{Trust No Bot: Discovering Personal Disclosures in Human-LLM Conversations in the Wild}},
author = {Mireshghallah, Niloofar and Antoniak, Maria and More, Yash and Choi, Yejin and Farnadi, Golnoosh},
year = {2024},
doi = {10.48550/arXiv.2407.11438},
eprint = {2407.11438},
archivePrefix = {arXiv},
url = {https://arxiv.org/abs/2407.11438}
}Reuse this entry
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