SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities (2305.11000v2)

Abstract: Multi-modal LLMs are regarded as a crucial step towards AGI and have garnered significant interest with the emergence of ChatGPT. However, current speech-LLMs typically adopt the cascade paradigm, preventing inter-modal knowledge transfer. In this paper, we propose SpeechGPT, a LLM 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/.

Analysis of SpeechGPT: Empowering LLMs with Intrinsic Cross-Modal Conversational Abilities

The paper at hand, "SpeechGPT: Empowering LLMs with Intrinsic Cross-Modal Conversational Abilities," introduces an innovative approach to integrating speech and language processing capabilities within a unified model, referred to as SpeechGPT. This work addresses inherent limitations of existing multi-modal LLMs by enhancing their ability to fluidly perceive and generate multi-modal content, essentially allowing seamless transition between text and speech modalities.

Contribution to Multi-Modal LLMs

The authors identify key challenges faced by current LLMs confined to a cascading paradigm where speech recognition and text generation are distinct, serial processes. This segmentation impedes the transfer of knowledge across modalities. SpeechGPT, on the other hand, amalgamates these processes to foster intrinsic cross-modal conversational capabilities.

One of the prominent contributions of this research is the SpeechInstruct dataset, a large-scale cross-modal speech instruction set that plays a pivotal role in training the model across speech and text modalities. Furthermore, this effort marks a significant step by constructing an initial foundation for unified multi-modal tasks through a system that handles both continuous and discrete signals effectively.

Three-Stage Training Strategy

The authors propose a three-stage training process designed to enhance SpeechGPT's performance in cross-modal tasks:

1. Modality-Adaptation Pre-Training: This initial phase utilizes unlabeled speech data to adapt the model towards speech input and discrete unit prediction, bridging the gap between speech and language understanding.
2. Cross-Modal Instruction Fine-Tuning: Here, the SpeechInstruct dataset is employed to fine-tune the model, enriching its handling of varying modalities by exposing it to paired cross-modal instruction data.
3. Chain-of-Modality Instruction Fine-Tuning: In this final tuning stage, the model is endowed with further cross-modal capabilities using parameter-efficient Low-Rank Adaptation (LoRA), targeting enhanced alignment across modalities.

Empirical Evaluation

The paper provides a comprehensive evaluation of SpeechGPT’s abilities through both human evaluation and case studies. The results underscore the model’s proficiency in undertaking multi-modal tasks, outperforming traditional approaches in terms of instruction-following capabilities across speech and text contexts. Notably, SpeechGPT demonstrates competence in spoken dialogue tasks, adhering to the Harmless, Helpful, Honest (HHH) criteria, crucially validating the effectiveness of the proposed cross-modal architecture.

Implications and Future Developments

The successful integration of speech and language capabilities within SpeechGPT opens avenues for exciting future developments in AI. By facilitating the bidirectional transfer of linguistic competencies across modalities, SpeechGPT can potentially underpin a new generation of multi-modal interfaces and applications. Future research could extend this architecture to encompass other sensor modalities, such as vision, thereby moving incrementally towards more generalized artificial intelligence systems.

Despite its achievements, the paper identifies limitations such as the model’s reliance on text-based processing for speech output and constraints in capturing nuanced paralinguistic features like emotion and prosody, which merit further investigation. The integration of these aspects may offer opportunities for more robust interactions in human-computer interfaces.

In conclusion, the insights drawn from the development of SpeechGPT offer significant contributions to AI’s progression, particularly marking advancements in the domain of multi-modal language understanding and synthesis. This paper lays a strong foundation for future research aimed at enhancing the intrinsic cross-modal capabilities of LLMs.