Papers by Dana Atzil-Slonim
Responsible Evaluation of AI for Mental Health (2026.acl-long)
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Hiba Arnaout, Anmol Goel, H. Andrew Schwartz, Steffen T. Eberhardt, Dana Atzil-Slonim, Gavin Doherty, Brian Schwartz, Wolfgang Lutz, Tim Althoff, Munmun De Choudhury, Hamidreza Jamalabadi, Raj Sanjay Shah, Flor Miriam Plaza-del-Arco, Dirk Hovy, Maria Liakata, Iryna Gurevych
| Challenge: | Existing approaches to evaluating AI tools in this domain remain fragmented and inconsistent. |
| Approach: | They propose a taxonomy of AI mental health support types that integrates clinical soundness, social context, and equity to provide a structured basis for evaluation. |
| Outcome: | The proposed framework integrates clinical soundness, social context, and equity, providing a structured basis for evaluation. |
Temporal reasoning for timeline summarisation in social media (2025.acl-long)
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| Challenge: | Existing temporal reasoning datasets focus on pair-wise event relationships. |
| Approach: | They propose a temporal reasoning dataset focused on temporal relationships among sequential events within narratives that combines temporal thinking with timeline summarisation through a knowledge distillation framework. |
| Outcome: | The proposed model achieves superior performance on mental health-related timeline summarisation tasks, highlighting the importance and generalisability of leveraging temporal reasoning to improve timeline summaries. |
Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity (2026.eacl-long)
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| Challenge: | Large language models (LLMs) have shown growing potential in offering emotional support, but their ability to deliver culturally sensitive support remains underexplored due to a lack of resources. |
| Approach: | They propose a large language model dataset that includes 1,729 distress messages, 1,523 cultural signals and 1,041 support strategies with fine-grained emotional and cultural annotations. |
| Outcome: | The proposed models outperform peer-reviewed models and lack cultural sensitivity. |
Combining Hierachical VAEs with LLMs for clinically meaningful timeline summarisation in social media (2024.findings-acl)
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| Challenge: | Existing studies have shown that social media users' posts can help identify depression, bipolar disorder or self-harm. |
| Approach: | They propose a hybrid abstractive summarisation approach combining hierarchical VAEs with LLMs to produce clinically meaningful summaries from social media timelines. |
| Outcome: | The proposed approach produces clinically meaningful summaries from social media user timelines, suitable for mental health monitoring. |
Predicting Client Emotions and Therapist Interventions in Psychotherapy Dialogues (2024.eacl-long)
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| Challenge: | Recent studies have focused on the role of client emotions and therapist interventions in predicting treatment outcomes from psychotherapy dialogues. |
| Approach: | They propose to model the therapist-intervention-prediction-based dialogue acts at the utterance-level using a pan-theoretical schema and fine-tuned language models. |
| Outcome: | The proposed model predicts the coherence between client self-reports on emotion and utterance-level emotions. |