@inproceedings{demeocq-etal-2026-attachment,
title = "The Attachment Index: Auditing Attachment Language Cues and Relational Safety Risks in Human-{LLM} Dialogue",
author = {Demeocq, Cyndie and
Prasad, Animesh and
Saeidi, Marzieh and
Goodall, Karen and
Ross, Bj{\"o}rn},
editor = "Zirikly, Aya and
Bar, Kfir and
MacAvaney, Sean and
Ireland, Molly and
Ophir, Yaakov and
Atzil-Slonim, Dana and
Varadarajan, Vasudha and
Bedrick, Steven and
Desmet, Bart",
booktitle = "Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology ({CLP}sych 2026)",
month = jul,
year = "2026",
address = "San Diego, California, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.clpsych-1.26/",
pages = "324--339",
ISBN = "979-8-89176-421-7",
abstract = "As conversational AI systems grow increasingly toward emotional support contexts, relational safety failures between users and chatbot remain under-measured. We present a psycholinguistic grounded framework for auditing attachment-relevant language cues. Our approach identifies when an LLM{'}s replies exhibit linguistic attachment cues and surface related patterns that may signal parasocial bonding, including anthropomorphism or over-dependence. We adapt the Adult Attachment Interview into two complementary, automatable lenses - attachment cues features and Gricean maxims - and combine them with psychologist-led annotation of multi-turn persona dialogues. Applying this framework, we observe that models can align with persona-intended attachment cue patterns. We also find that judge-LLMs alone are unreliable, highlighting the need for psychologist-in-the-loop evaluation. The 25 psychologist-led annotated conversations revealed risks, including boundary blurring and missed opportunities for appropriate referral or triage. These insights motivate attachment-aware safeguards - such as non-personification, boundary language, and explicit referral mechanisms - to reduce mis-attunement and over-attachment in LLM conversational settings."
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<abstract>As conversational AI systems grow increasingly toward emotional support contexts, relational safety failures between users and chatbot remain under-measured. We present a psycholinguistic grounded framework for auditing attachment-relevant language cues. Our approach identifies when an LLM’s replies exhibit linguistic attachment cues and surface related patterns that may signal parasocial bonding, including anthropomorphism or over-dependence. We adapt the Adult Attachment Interview into two complementary, automatable lenses - attachment cues features and Gricean maxims - and combine them with psychologist-led annotation of multi-turn persona dialogues. Applying this framework, we observe that models can align with persona-intended attachment cue patterns. We also find that judge-LLMs alone are unreliable, highlighting the need for psychologist-in-the-loop evaluation. The 25 psychologist-led annotated conversations revealed risks, including boundary blurring and missed opportunities for appropriate referral or triage. These insights motivate attachment-aware safeguards - such as non-personification, boundary language, and explicit referral mechanisms - to reduce mis-attunement and over-attachment in LLM conversational settings.</abstract>
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%0 Conference Proceedings
%T The Attachment Index: Auditing Attachment Language Cues and Relational Safety Risks in Human-LLM Dialogue
%A Demeocq, Cyndie
%A Prasad, Animesh
%A Saeidi, Marzieh
%A Goodall, Karen
%A Ross, Björn
%Y Zirikly, Aya
%Y Bar, Kfir
%Y MacAvaney, Sean
%Y Ireland, Molly
%Y Ophir, Yaakov
%Y Atzil-Slonim, Dana
%Y Varadarajan, Vasudha
%Y Bedrick, Steven
%Y Desmet, Bart
%S Proceedings of the 10th Workshop on Computational Linguistics and Clinical Psychology (CLPsych 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-421-7
%F demeocq-etal-2026-attachment
%X As conversational AI systems grow increasingly toward emotional support contexts, relational safety failures between users and chatbot remain under-measured. We present a psycholinguistic grounded framework for auditing attachment-relevant language cues. Our approach identifies when an LLM’s replies exhibit linguistic attachment cues and surface related patterns that may signal parasocial bonding, including anthropomorphism or over-dependence. We adapt the Adult Attachment Interview into two complementary, automatable lenses - attachment cues features and Gricean maxims - and combine them with psychologist-led annotation of multi-turn persona dialogues. Applying this framework, we observe that models can align with persona-intended attachment cue patterns. We also find that judge-LLMs alone are unreliable, highlighting the need for psychologist-in-the-loop evaluation. The 25 psychologist-led annotated conversations revealed risks, including boundary blurring and missed opportunities for appropriate referral or triage. These insights motivate attachment-aware safeguards - such as non-personification, boundary language, and explicit referral mechanisms - to reduce mis-attunement and over-attachment in LLM conversational settings.
%U https://aclanthology.org/2026.clpsych-1.26/
%P 324-339
Markdown (Informal)
[The Attachment Index: Auditing Attachment Language Cues and Relational Safety Risks in Human-LLM Dialogue](https://aclanthology.org/2026.clpsych-1.26/) (Demeocq et al., CLPsych 2026)
ACL