ContextQFormer: A New Context Modeling Method for Multi-Turn Multi-Modal Conversations

• URL: http://arxiv.org/abs/2505.23121v2
• Date: Thu, 17 Jul 2025 16:49:08 GMT
• Title: ContextQFormer: A New Context Modeling Method for Multi-Turn Multi-Modal Conversations
• Authors: Yiming Lei, Zhizheng Yang, Zeming Liu, Haitao Leng, Shaoguo Liu, Tingting Gao, Qingjie Liu, Yunhong Wang,
• Abstract summary: We introduce a context modeling module, termed ContextQFormer, to enhance the presentation of contextual information.<n>To facilitate further research, we build a new multi-turn multi-modal dialogue dataset (TMDialog) for pre-training, instruction-tuning, and evaluation.<n>In addition, ContextQFormer is compared with three baselines on TMDialog and experimental results illustrate that ContextQFormer achieves an improvement of 2%-4% in available rate over baselines.
• Score: 38.40471808648207
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Multi-modal large language models have demonstrated remarkable zero-shot abilities and powerful image-understanding capabilities. However, the existing open-source multi-modal models suffer from the weak capability of multi-turn interaction, especially for long contexts. To address the issue, we first introduce a context modeling module, termed ContextQFormer, which utilizes a memory block to enhance the presentation of contextual information. Furthermore, to facilitate further research, we carefully build a new multi-turn multi-modal dialogue dataset (TMDialog) for pre-training, instruction-tuning, and evaluation, which will be open-sourced lately. Compared with other multi-modal dialogue datasets, TMDialog contains longer conversations, which supports the research of multi-turn multi-modal dialogue. In addition, ContextQFormer is compared with three baselines on TMDialog and experimental results illustrate that ContextQFormer achieves an improvement of 2%-4% in available rate over baselines.

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