Papers by Subin Lee
What if you said that differently?: How Explanation Formats Affect Human Feedback Efficacy and User Perception (2024.naacl-long)
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| Challenge: | Question answering models can often be black boxes, as their reasoning process is mostly opaque. |
| Approach: | They analyze the effect of rationales generated by QA models on user feedback and how well they enable users to understand and trust model answers. |
| Outcome: | The proposed model can be used to improve model responses by removing feedback from end users and enhancing model outputs by using natural language feedback. |
Multimodal Cognitive Reframing Therapy via Multi-hop Psychotherapeutic Reasoning (2025.naacl-long)
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| Challenge: | Existing studies have focused on text-based cognitive reframing, but neglected the importance of non-verbal evidence in real-life therapy. |
| Approach: | They propose a dataset that pairs each GPT-4-generated dialogue with an image that reflects the virtual client’s facial expressions to better mirror real psychotherapy, where facial expression leads to interpreting implicit emotional evidence. |
| Outcome: | The proposed approach outperforms existing methods with LLMs and vision-language models and provides more thoughtful and empathetic suggestions. |
MIRROR: Multimodal Cognitive Reframing Therapy for Rolling with Resistance (2025.emnlp-main)
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| Challenge: | Recent studies have explored the use of large language models (LLMs) in psychotherapy, however text-based cognitive behavioral therapy models struggle with client resistance, which weakens therapeutic alliance. |
| Approach: | They propose a multimodal approach that incorporates nonverbal cues and a synthetic dataset that pairs each client’s statements with corresponding facial images to train vision language models. |
| Outcome: | The proposed approach outperforms existing text-based cognitive behavioral therapy models in managing client resistance and fostering therapeutic alliance. |
ExpertQA: Expert-Curated Questions and Attributed Answers (2024.naacl-long)
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| Challenge: | a recent study examined the attribution and factuality of language models in domains . experts from various fields are using large language models for information-seeking scenarios . |
| Approach: | They evaluate language models' attribution and factuality by bringing domain experts in the loop . they collect expert-curated questions from 484 participants across 32 fields of study . |
| Outcome: | The results show that language models can provide factually correct answers in high-stakes fields, but they can also be harmful to experts. |