Summary of ‘no’ Matters: Out-of-distribution Detection in Multimodality Long Dialogue, by Rena Gao and Xuetong Wu and Siwen Luo and Caren Han and Feng Liu
‘No’ Matters: Out-of-Distribution Detection in Multimodality Long Dialogue
by Rena Gao, Xuetong Wu, Siwen Luo, Caren Han, Feng Liu
First submitted to arxiv on: 31 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers aim to improve the user experience in open-domain dialogue systems by detecting out-of-distribution (OOD) dialogues and images efficiently. They propose a novel scoring framework called Dialogue Image Aligning and Enhancing Framework (DIAEF), which integrates visual language models with new scores to detect OOD in two scenarios: mismatches between dialogue and image inputs, and input pairs with previously unseen labels. Experimental results on various benchmarks show that integrating image and multi-round dialogue OOD detection is more effective for identifying unknown labels than using either modality alone. The proposed score also demonstrates strong robustness in long dialogues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make conversations between humans and computers better by finding when the conversation doesn’t match what’s expected. It uses a new way to combine computer vision models with dialogue models to detect when something is off. This makes chatbots more helpful and adaptable. The results show that combining image and text can help identify unknown labels, which is important for making conversations more natural. |
