Papers with real-time conversations

2 papers
Utterance-level Detection Framework for LLM-Involved Content Detection in Conversational Setting (2026.eacl-long)

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Challenge: Existing methods focus on static, document-level content, overlooking the dynamic nature of dialogues.
Approach: They propose an utterance-level detection framework which integrates features from individual and combined analysis of dialogue participants’ responses to detect LLM-generated text under conversational setting.
Outcome: The proposed framework achieves 98.14% accuracy with high inference speed and extensive results on different models and settings.
Beyond the Turn-Based Game: Enabling Real-Time Conversations with Duplex Models (2024.emnlp-main)

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Challenge: Large language models (LLMs) are increasingly permeating daily lives and require real-time interactions that mirror human conversations.
Approach: They propose to use time-division-multiplexing to process queries and responses pseudo-simultaneously.
Outcome: The proposed model can listen to users while generating output and adjust to provide instant feedback.

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