• Author(s): Shengqiong Wu, Hao Fei, Xiangtai Li, Jiayi Ji, Hanwang Zhang, Tat-Seng Chua, Shuicheng Yan

The paper titled “Towards Semantic Equivalence of Tokenization in Multimodal LLM” addresses the critical issue of achieving semantic equivalence in the tokenization process within multimodal large language models (MLLMs). Tokenization, the process of converting input data into a format suitable for machine learning models, is essential for the performance of MLLMs. However, existing vision tokenizers often fragment visual input excessively, compromising the semantic integrity of the visual data.
To tackle this problem, the paper introduces a novel dynamic Semantic-Equivalent Vision Tokenizer (SeTok). This tokenizer groups visual features into semantic units using a dynamic clustering algorithm, which adjusts the number of tokens based on the complexity of the image. This approach ensures that the resulting vision tokens preserve semantic integrity and capture both low-frequency and high-frequency visual features effectively.

The proposed MLLM, named Setokim, incorporates the SeTok tokenizer and demonstrates superior performance across various tasks. The experimental results show that Setokim significantly outperforms existing methods in terms of accuracy and semantic alignment. The dynamic clustering algorithm used in SeTok allows for flexible and efficient tokenization, which is crucial for maintaining the semantic coherence of the visual input.

The paper provides a comprehensive evaluation of the SeTok tokenizer, including quantitative assessments on standard benchmarks and qualitative analyses. These evaluations highlight the improvements in semantic understanding and the ability of the model to generate contextually appropriate responses, even when dealing with complex multimodal inputs. In conclusion, the paper “Towards Semantic Equivalence of Tokenization in Multimodal LLM” presents a significant advancement in the field of natural language processing. By ensuring semantic equivalence in tokenization, the proposed SeTok method enhances the performance and reliability of multimodal LLMs. This research contributes to the development of more robust and versatile language models capable of handling a wide range of input modalities with improved semantic understanding.