Auto-Encoding Morph-Tokens for Multimodal LLM

For multimodal LLMs, the synergy of visual comprehension (textual output) and generation (visual output) presents an ongoing challenge. This is due to a conflicting objective: for comprehension, an MLLM needs to abstract the visuals; for generation, it needs to preserve the visuals as much as possible. Thus, the objective is a dilemma for visual-tokens. To resolve the conflict, we propose encoding images into morph-tokens to serve a dual purpose: for comprehension, they act as visual prompts instructing MLLM to generate texts; for generation, they take on a different, non-conflicting role as complete visual-tokens for image reconstruction, where the missing visual cues are recovered by the MLLM. Extensive experiments show that morph-tokens can achieve a new SOTA for multimodal comprehension and generation simultaneously. Our project is available at https: //github. com/DCDmllm/MorphTokens.

Authors

• Kaihang Pan
• Siliang Tang
• Juncheng Li 0006
• Zhaoyu Fan
• Wei Chow
• Shuicheng Yan
• Tat-Seng Chua
• Yueting Zhuang
• Hanwang Zhang

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