  Large language models (LLMs) have demonstrated remarkable language abilities.
GPT-4, based on advanced LLMs, exhibits extraordinary multimodal capabilities
beyond previous visual language models. We attribute this to the use of more
advanced LLMs compared with previous multimodal models. Unfortunately, the
model architecture and training strategies of GPT-4 are unknown. To endow LLMs
with multimodal capabilities, we propose X-LLM, which converts Multi-modalities
(images, speech, videos) into foreign languages using X2L interfaces and inputs
them into a large Language model (ChatGLM). Specifically, X-LLM aligns multiple
frozen single-modal encoders and a frozen LLM using X2L interfaces, where ``X''
denotes multi-modalities such as image, speech, and videos, and ``L'' denotes
languages. X-LLM's training consists of three stages: (1) Converting Multimodal
Information: The first stage trains each X2L interface to align with its
respective single-modal encoder separately to convert multimodal information
into languages. (2) Aligning X2L representations with the LLM: single-modal
encoders are aligned with the LLM through X2L interfaces independently. (3)
Integrating multiple modalities: all single-modal encoders are aligned with the
LLM through X2L interfaces to integrate multimodal capabilities into the LLM.
Our experiments show that X-LLM demonstrates impressive multimodel chat
abilities, sometimes exhibiting the behaviors of multimodal GPT-4 on unseen
images/instructions, and yields a 84.5\% relative score compared with GPT-4 on
a synthetic multimodal instruction-following dataset. And we also conduct
quantitative tests on using LLM for ASR and multimodal ASR, hoping to promote
the era of LLM-based speech recognition.
