Key Points
1. AnyGPT is introduced as an any-to-any multimodal language model that utilizes discrete representations to process various modalities, including speech, text, images, and music.
2. The model can be trained stably without altering the existing large language model (LLM) architecture or training paradigms, focusing on data-level preprocessing and seamless integration of new modalities, similar to adding new languages.
3. AnyGPT uses multimodal tokenizers to compress raw multimodal data into discrete semantic tokens, allowing the core LLM to unify tasks such as perception, understanding, reasoning, and generation at the semantic level.
4. The model is capable of facilitating any-to-any multimodal conversation and achieves performance comparable to specialized models across various modalities, proving the effectiveness of discrete representations in unifying multiple modalities.
5. AnyGPT is evaluated across tasks such as image understanding, image generation, automatic speech recognition (ASR), text-to-speech (TTS), and music understanding and generation, achieving promising results in zero-shot evaluations for multimodal understanding and generation tasks.
6. The model relies on a two-stage framework for high-fidelity generation involving semantic information modeling and perceptual information modeling, enhancing the quality of generated multimodal content.
7. AnyGPT has been pre-trained on the AnyInstruct-108k dataset, which consists of 108k high-quality multimodal dialogues featuring a variety of multimodal combinations, and demonstrates the capability and potential in any-to-any multimodal dialogue.
8. Recommendations are made for the development of a comprehensive benchmark to evaluate any-to-any multimodal large language models and to enhance tokenizers and extend the context for practical use in audio output and complex interactions.
9. The paper highlights the potential strategies to improve multimodal fusion, enhance tokenizers, and address the challenges posed by longer context for practical use.
Summary
Introduction of AnyGPT
The paper introduces AnyGPT, a model for any-to-any multimodal language processing using discrete representations for speech, text, images, and music. It utilizes data-level preprocessing and a text-centric dataset for pre-training. The model achieves large-scale multimodal understanding and generation, handling arbitrary combinations of multimodal inputs and outputs. Experimental results show that AnyGPT effectively handles any-to-any multimodal conversations and performs comparably to specialized models. It overcomes challenges faced by existing multimodal models by unifying diverse modalities within a language model.
AnyGPT's Framework
The paper introduces a framework composed of multimodal tokenizers, a backbone language model, and multimodal de-tokenizers to enable comprehensive multimodal processing. AnyGPT's discrete representations allow for stable training without altering the existing large language model architecture or training paradigms. The model produces high-quality multimodal outputs through autoregressive processing at the semantic level and post-processing by non-autoregressive models at the perceptual level.
Multimodal Dataset and Future Enhancements
The paper also describes the construction of a multimodal dataset, AnyInstruct-108k, comprising 108k multi-turn conversations covering various modalities. Additionally, it provides detailed data-level preprocessing used for training AnyGPT and discusses the performance of the model in handling any-to-any multimodal conversations compared to specialized models across all modalities. The paper highlights the construction of a benchmark for evaluating any-to-any multimodal large language models and identifies potential areas for future enhancements, such as improving the tokenizer and extending the context for better multimodal content processing.
Reference: https://arxiv.org/abs/2402.122...