arXiv:2606.04591v1 Announce Type: new Abstract: With the widespread adoption of multi-modal communication platforms, long-form dialogues interleaving text and images have become increasingly common. Users often need to retrieve coherent dialogue fragments related to specific topics, rather than isolated utterances. We propose Fine-grained Fragment Retrieval (FFR), which locates semantically relevant multi-utterance, multi-image fragments in multi-modal long-form dialogues. We explore two settings: (1) FFR within Single-Dialogue, retrieving fragments from a given dialogue; and (2) FFR within Di
The proliferation of multi-modal communication platforms necessitates more advanced retrieval methods for complex, long-form dialogues that interleave text and images.
This development improves information accessibility and analysis within multi-modal interactions, enhancing the utility of AI systems in complex communication environments.
The ability to retrieve semantically relevant, multi-utterance, multi-image fragments within dialogues rather than isolated utterances fundamentally improves how AI can process and understand conversational context.
- · AI developers
- · Generative AI platforms
- · Customer service industries
- · Knowledge management systems
- · Inefficient search algorithms
- · Monolithic document retrieval systems
Improved performance and user experience for multi-modal dialogue systems.
Accelerated development of more sophisticated AI assistants capable of understanding and synthesizing complex conversations.
Potential for new applications in areas like digital forensics and intelligent content curation based on contextual understanding.
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Read at arXiv cs.CL