{
 "id": "multimodal-reframing/mirror",
 "owner": "multimodal-reframing",
 "name": "mirror",
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 "last_modified": "2025-11-18T05:30:53.000Z",
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 "tasks": [
  "text-generation",
  "image-text-to-text"
 ],
 "citation": "@inproceedings{kim-etal-2025-mirror,\n    title = \"{MIRROR}: Multimodal Cognitive Reframing Therapy for Rolling with Resistance\",\n    author = \"Kim, Subin  and Kim, Hoonrae  and Lee, Jihyun  and Jeon, Yejin  and Lee, Gary\",\n    editor = \"Christodoulopoulos, Christos  and Chakraborty, Tanmoy  and Rose, Carolyn  and Peng, Violet\",\n    booktitle = \"Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing\",\n    month = nov,\n    year = \"2025\",\n    address = \"Suzhou, China\",\n    publisher = \"Association for Computational Linguistics\",\n    url = \"https://aclanthology.org/2025.emnlp-main.751/\",\n    doi = \"10.18653/v1/2025.emnlp-main.751\",\n    pages = \"14851--14880\",\n    ISBN = \"979-8-89176-332-6\",\n    abstract = \"Recent studies have explored the use of large language models (LLMs) in psychotherapy; however, text-based cognitive behavioral therapy (CBT) models often struggle with client resistance, which can weaken therapeutic alliance. To address this, we propose a multimodal approach that incorporates nonverbal cues, which allows the AI therapist to better align its responses with the client{'}s negative emotional state.Specifically, we introduce a new synthetic dataset, Mirror (Multimodal Interactive Rolling with Resistance), which is a novel synthetic dataset that pairs each client{'}s statements with corresponding facial images. Using this dataset, we train baseline vision language models (VLMs) so that they can analyze facial cues, infer emotions, an",
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