The Silent Thought: Modeling Internal Cognition in Full-Duplex Spoken Dialogue Models via Latent Reasoning
Abstract
During conversational interactions, humans subconsciously engage in concurrent thinking while listening to a speaker. Although this internal cognitive processing may not always manifest as explicit linguistic structures, it is instrumental in formulating high-quality responses. Inspired by this cognitive phenomenon, we propose a novel Full-duplex LAtent and Internal Reasoning method named FLAIR that conducts latent thinking simultaneously with speech perception. Unlike conventional "thinking" mechanisms in NLP, which require post-hoc generation, our approach aligns seamlessly with spoken dialogue systems: during the user's speaking phase, it recursively feeds the latent embedding output from the previous step into the next step, enabling continuous reasoning that strictly adheres to causality without introducing additional latency. To enable this latent reasoning, we design an Evidence Lower Bound-based objective that supports efficient supervised finetuning via teacher forcing, circumventing the need for explicit reasoning annotations. Experiments demonstrate the effectiveness of this think-while-listening design, which achieves competitive results on a range of speech benchmarks. Furthermore, FLAIR robustly handles conversational dynamics and attains competitive performance on full-duplex interaction metrics.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The main contribution of this paper is the introduction of FLAIR, a framework that enables full-duplex spoken dialogue models to perform latent reasoning concurrently with speech perception, enhancing response quality and conversational dynamics. This innovative approach addresses a critical gap in current dialogue systems, allowing for more human-like interactions and setting a foundation for future research in the field.
Methodology
The proposed methodology, FLAIR, introduces a novel approach to full-duplex spoken dialogue systems by integrating latent reasoning during the listening phase. This is achieved through an Evidence Lower Bound (ELBO)-based objective that allows for efficient supervised fine-tuning without requiring explicit reasoning annotations. The use of a Global-aware Expert model to derive latent embeddings is innovative, as it leverages the entire dialogue context to enhance response generation. The recursive feeding of latent embeddings during user speech is a significant departure from traditional autoregressive models, allowing for continuous reasoning without latency.
Results & evaluation
The experimental results demonstrate the effectiveness of FLAIR across multiple benchmarks, showcasing improvements in response quality and conversational dynamics. The paper provides a comprehensive evaluation on various tasks, including factual knowledge and multi-turn question answering, and compares FLAIR against existing models. The results indicate that FLAIR achieves competitive performance, particularly in scenarios requiring reasoning, which underscores its practical applicability in real-world dialogue systems.
Reproducibility
The paper outlines the training process and architecture in detail, including the data generation methods and the training pipeline. However, the lack of a publicly available code repository or demo limits reproducibility, as external researchers cannot easily implement or test the proposed model.
Limitations
One limitation is the reliance on synthetic data for training, which may not fully capture the nuances of real-world conversational interactions. Additionally, the paper does not address potential biases in the generated datasets or the implications of using large-scale models in diverse applications. The absence of a demo or project URL also hinders practical engagement with the work.
Broader impact
The advancements presented in this paper have the potential to significantly enhance human-computer interaction, making conversational agents more responsive and capable of handling complex dialogue scenarios. This could lead to more natural and efficient communication in various applications, including customer service, virtual assistants, and educational tools. The main contribution of this paper is the introduction of FLAIR, a framework that enables full-duplex spoken dialogue models to perform latent reasoning concurrently with speech perception, enhancing response quality and conversational dynamics. This innovative approach addresses a critical gap in current dialogue systems, allowing for more human-like interactions and setting a foundation for future research in the field.
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