AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling
(2402.12226)Abstract
We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. AnyGPT can be trained stably without any alterations to the current LLM architecture or training paradigms. Instead, it relies exclusively on data-level preprocessing, facilitating the seamless integration of new modalities into LLMs, akin to the incorporation of new languages. We build a multimodal text-centric dataset for multimodal alignment pre-training. Utilizing generative models, we synthesize the first large-scale any-to-any multimodal instruction dataset. It consists of 108k samples of multi-turn conversations that intricately interweave various modalities, thus equipping the model to handle arbitrary combinations of multimodal inputs and outputs. Experimental results demonstrate that AnyGPT is capable of facilitating any-to-any multimodal conversation while achieving performance comparable to specialized models across all modalities, proving that discrete representations can effectively and conveniently unify multiple modalities within a language model. Demos are shown in https://junzhan2000.github.io/AnyGPT.github.io/
Overview
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AnyGPT introduces an advanced framework for integrating multimodal inputs and outputs into LLMs, enhancing their capabilities to understand and generate content across text, speech, images, and music.
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The model employs discrete sequence modeling, utilizing a suite of tokenizers for each modality, to transform modality-specific signals into discrete tokens that can be processed by LLMs without altering their underlying structure.
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AnyGPT leverages the AnyInstruct-108k dataset, synthesized through advanced generative models, to navigate and generate coherent multimodal conversations, addressing the scarcity of multimodal aligned datasets.
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Experimental validation shows that AnyGPT competes with specialized models in handling multimodal conversations, underscoring the potential of discrete representations in bridging various human-computer interaction modes within a unified framework.
Advances in Any-to-Any Multimodal Conversations with AnyGPT
Introduction
In the evolving landscape of machine learning, the integration of multimodal inputs and outputs into LLMs stands as a significant challenge. Traditionally, LLMs have excelled in handling text-based data but faltered when engaging with more varied modalities such as images, speech, and music. The paper introduces AnyGPT, a novel approach to any-to-any multimodal LLM. Utilizing discrete sequence modeling, it manages to seamlessly integrate a diverse set of modalities, retaining the architecture and training paradigms of existing LLMs. This is achieved through a combination of multimodal tokenization, generative model synthesis, and a refined data alignment process.
Multimodal Encapsulation via Tokenization
AnyGPT distinguishes itself by employing discrete representations for encoding and decoding multimodal data. This method hinges on transforming continuous modality-specific signals into a sequence of discrete tokens, which can then be processed autoregressively by the LLM. These tokens encapsulate semantic information, enabling the model to understand and generate content across text, speech, images, and music without altering the LLM's underlying structure. The implementation utilizes a suite of tokenizers for each modality, with the encoding strategy tailored to the specific characteristics of the data it represents.
Semantics-Driven Data Synthesis
Addressing the scarcity of multimodal aligned datasets, AnyGPT introduces AnyInstruct-108k, an instruction dataset synthesized through advanced generative models. It encompasses 108k samples of multi-turn conversations with meticulously interleaved multimodal elements. This curated dataset equips AnyGPT with the capacity to navigate complex conversational contexts involving any combination of the supported modalities. This approach not only enriches the model's understanding but also its ability to generate coherent and contextually appropriate multimodal responses.
Experimental Validation
The model's capabilities are rigorously tested across various tasks, showcasing its adeptness at handling multimodal conversations. Through zero-shot performance metrics, AnyGPT demonstrates a comparable efficacy to specialized models dedicated to single modalities. This evidence underscores the viability of discrete representations in bridging different modes of human-computer interaction within a unified linguistic framework.
Theoretical and Practical Implications
The conceptualization of AnyGPT lays a foundation for theoretical advancements in understanding how discrete representations can encapsulate and convey multimodal information. Practically, it paves the way for the development of more sophisticated and versatile AI systems capable of engaging in any-to-any multimodal dialogues. This holds promise for enhancing user experience in applications ranging from virtual assistants to interactive AI in gaming and education.
Looking Forward
Despite its impressive capabilities, AnyGPT invites further exploration and optimization. Future efforts could focus on expanding the model's comprehension of modality-specific nuances and improving the fidelity of generated multimodal content. Additionally, the creation of more comprehensive benchmarks for evaluating any-to-any multimodal interactions remains a critical area for ongoing research.
AnyGPT marks a significant step towards the seamless integration of multiple modalities within LLM frameworks. Its innovative approach to discrete sequence modeling not only enriches the model's interactive capabilities but also opens new avenues for the development of genuinely multimodal AI systems.