FLM-Audio: 自然独白提升原生全双工聊天机器人通过双训练
FLM-Audio: Natural Monologues Improves Native Full-Duplex Chatbots via Dual Training
AI总结：
FLM-Audio通过双训练策略和连续独白提升全双工聊天机器人的响应质量和体验。
摘要：
全双工对话模型旨在同时听和说，以快速响应动态用户输入。在不同的全双工解决方案中，原生解决方案在每个时间步合并多个通道，实现最低的延迟。然而，现有设计将文本独白句子分解为词级对齐音频流，这会降低语言建模能力。为了帮助解决这个问题，我们引入了
英文摘要：
Full-duplex dialog models aim to listen and speak simultaneously, delivering rapid responses to dynamic user input. Among different solutions to full-duplexity, a native solution merges multiple channels in each time step, achieving the lowest latency. However, prevailing designs break down the textual monologue sentences for word-level alignment with audio streams, which degrades language modeling abilities. To help address this issue, we introduce "contiguous monologues", which are composed by continuous sentences and "waiting" intervals, mimicking human-like cognitive behavior in dialogs. We find a proper training paradigm to be critical for semantically aligning contiguous monologues with audio. To this end, we develop a "dual" training paradigm that alternates the position of the monologues, either leading or trailing the audio, across different training stages. A combination of our contiguous monologue and dual training strategy is applied in developing FLM-Audio, our 7B spoken dialog chatbot with native full-duplexity. As confirmed by experimental results, FLM-Audio achieves superior response qualities and chatting experiences while requiring significantly less training data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
