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From the 1 of 31 linked papers with an AI index.

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20242026
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cs.CL2026

BayLing-Duplex: Native Full-Duplex Speech Dialogue with a Single Autoregressive LLM

Qingkai Fang, Shoutao Guo, Yang Feng

Real-time, full-duplex speech interaction is a key feature of next-generation spoken chatbots, allowing the model to listen and speak at the same time and to handle natural phenome…

cs.CL2026

FreezeEmpath: Efficient Training for Empathetic Spoken Chatbots with Frozen LLMs

Yun Hong, Yan Zhou, Yang Feng

Empathy is essential for fostering natural interactions in spoken dialogue systems, as it enables machines to recognize the emotional tone of human speech and deliver empathetic re…

cs.CL2026

Language on Demand, Knowledge at Core: Composing LLMs with Encoder-Decoder Translation Models for Extensible Multilinguality

Mengyu Bu, Yang Feng

Large language models (LLMs) exhibit strong general intelligence, yet their multilingual performance remains highly imbalanced. Although LLMs encode substantial cross-lingual knowl…

cs.CL2026

SpecBound: Adaptive Bounded Self-Speculation with Layer-wise Confidence Calibration

Zhuofan Wen, Yang Feng

Speculative decoding has emerged as a promising approach to accelerate autoregressive inference in large language models (LLMs). Self-draft methods, which leverage the base LLM its…

cs.CL2026

Efficient Training for Cross-lingual Speech Language Models

Yan Zhou, Qingkai Fang, Yun Hong +1

Currently, large language models (LLMs) predominantly focus on the text modality. To enable more natural human-AI interaction, speech LLMs are emerging, but building effective end-…

cs.CL2025

IG-Pruning: Input-Guided Block Pruning for Large Language Models

Kangyu Qiao, Shaolei Zhang, Yang Feng

With the growing computational demands of large language models (LLMs), efficient inference has become increasingly critical for practical deployment. Depth pruning has emerged as…