# Do We Talk to Robots Like Therapists, and Do They Respond Accordingly? Language Alignment in AI Emotional Support

arXiv: https://arxiv.org/abs/2506.16473 · HTML: https://mlsanity.com/paper/2506.16473/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 78 references that are in the map.
- First posted on arXiv 19 Jun 2025.

## Builds on (most cited first, 16 of 16)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support](https://mlsanity.com/paper/2009.08441/) (2020)
- [GPT-4 Emulates Average-Human Emotional Cognition from a Third-Person Perspective](https://mlsanity.com/paper/2408.13718/) (2024)
- [VITA: A Multi-modal LLM-based System for Longitudinal, Autonomous, and Adaptive Robotic Mental Well-being Coaching](https://mlsanity.com/paper/2312.09740/) (2023)
- [Steering Conversational Large Language Models for Long Emotional Support Conversations](https://mlsanity.com/paper/2402.10453/) (2024)
- [From "um" to "yeah": Producing, predicting, and regulating information flow in human conversation](https://mlsanity.com/paper/2403.08890/) (2024)
- [Appropriateness of LLM-equipped Robotic Well-being Coach Language in the Workplace: A Qualitative Evaluation](https://mlsanity.com/paper/2401.14935/) (2024)
- [A Robot-Led Intervention for Emotion Regulation: From Expression to Reappraisal](https://mlsanity.com/paper/2503.18243/) (2025)
- [What People Share With a Robot When Feeling Lonely and Stressed and How It Helps Over Time](https://mlsanity.com/paper/2504.02991/) (2025)
- [Exploring Emotion-Sensitive LLM-Based Conversational AI](https://mlsanity.com/paper/2502.08920/) (2025)
- [Comparing Self-Disclosure Themes and Semantics to a Human, a Robot, and a Disembodied Agent](https://mlsanity.com/paper/2504.06374/) (2025)
- [Critical Insights about Robots for Mental Wellbeing](https://mlsanity.com/paper/2506.13739/) (2025)

## Built on by (most cited first, 1 of 1)

- [DialogGuard: Multi-Agent Psychosocial Safety Evaluation of Sensitive LLM Responses](https://mlsanity.com/paper/2512.02282/) (2025)

## Abstract (arXiv)

As conversational agents increasingly engage in emotionally supportive dialogue, it is important to understand how closely their interactions resemble those in traditional therapy settings. This study investigates whether the concerns shared with a robot align with those shared in human-to-human (H2H) therapy sessions, and whether robot responses semantically mirror those of human therapists. We analyzed two datasets: one of interactions between users and professional therapists (Hugging Face's NLP Mental Health Conversations), and another involving supportive conversations with a social robot (QTrobot from LuxAI) powered by a large language model (LLM, GPT-3.5). Using sentence embeddings and K-means clustering, we assessed cross-agent thematic alignment by applying a distance-based cluster-fitting method that evaluates whether responses from one agent type map to clusters derived from the other, and validated it using Euclidean distances. Results showed that 90.88% of robot conversation disclosures could be mapped to clusters from the human therapy dataset, suggesting shared topical structure. For matched clusters, we compared the subjects as well as therapist and robot responses using Transformer, Word2Vec, and BERT embeddings, revealing strong semantic overlap in subjects' disclosures in both datasets, as well as in the responses given to similar human disclosure themes across agent types (robot vs. human therapist). These findings highlight both the parallels and boundaries of robot-led support conversations and their potential for augmenting mental health interventions.