Papers by Sehun Lee

2 papers
Think, Verbalize, then Speak: Bridging Complex Thoughts and Comprehensible Speech (2025.emnlp-main)

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Challenge: Existing approaches to decouple LLMs from spoken communication produce suboptimal results due to mismatches between optimal textual and verbal delivery.
Approach: They propose a framework that decouples reasoning from spoken delivery to preserve the full reasoning capacity of LLMs.
Outcome: The proposed framework preserves full reasoning capacity of large language models . it improves speech naturalness and conciseness with minimal impact on reasoning .
Behavior-SD: Behaviorally Aware Spoken Dialogue Generation with Large Language Models (2025.naacl-long)

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Challenge: Spoken dialogues lack explicit modeling of behavior traits that are often overlooked in language models . et al.: our work opens new possibilities for developing behaviorally-aware dialogue systems .
Approach: They propose a large-scale dataset with over 100K spoken dialogues (2,164 hours) they propose BeDLM, the first dialogue model capable of generating natural conversations .
Outcome: The proposed model outperforms baseline models in generating natural dialogues . the proposed model can generate natural conversations conditioned on behavioral and narrative contexts - a key feature of spoken language models .

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