SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal
  Conversational Abilities
Authors: name: Dong Zhang, name: Shimin Li, name: Xin Zhang, name: Jun Zhan, name: Pengyu Wang, name: Yaqian Zhou, name: Xipeng Qiu

Multi-modal large language models are regarded as a crucial step towards
Artificial General Intelligence (AGI) and have garnered significant interest
with the emergence of ChatGPT. However, current speech-language models
typically adopt the cascade paradigm, preventing inter-modal knowledge
transfer. In this paper, we propose SpeechGPT, a large language model with
intrinsic cross-modal conversational abilities, capable of perceiving and
generating multi-model content. With discrete speech representations, we first
construct SpeechInstruct, a large-scale cross-modal speech instruction dataset.
Additionally, we employ a three-stage training strategy that includes
modality-adaptation pre-training, cross-modal instruction fine-tuning, and
chain-of-modality instruction fine-tuning. The experimental results demonstrate
that SpeechGPT has an impressive capacity to follow multi-modal human
instructions and highlight the potential of handling multiple modalities with
one model. Demos are shown in https://0nutation.github.io/SpeechGPT.github.io/.