'No' Matters: Out-of-Distribution Detection in Multimodality Long Dialogue

Rena Wei Gao, Xuetong Wu, Caren Han, Siwen Luo, Feng Liu

Keywords: Natural Language Processing, Out-of-distribution Detection, Machine Learning, Multimodality Dialogue

TL;DR: This paper introduces the Dialogue Image Aligning and Enhancing Framework (DIAEF) by detecting out-of-distribution dialogues and images through a novel scoring method that identifies mismatched input pairs and unseen labels.

Abstract: Out-of-distribution (OOD) detection in multimodal contexts is essential for identifying deviations in combined inputs from different modalities, particularly in applications like open-domain dialogue systems or real-life dialogue interactions. This paper aims to improve the user experience that involves multi-round long dialogues by efficiently detecting OOD dialogues and images. We introduce a novel scoring framework named **D**ialogue **I**mage **A**ligning and **E**nhancing **F**ramework (DIAEF) that integrates the visual language models with the novel proposed scores that detect OOD in two key scenarios (1) mismatches between the dialogue and image input pair and (2) input pairs with previously unseen labels. Our experimental results, derived from various benchmarks, demonstrate that integrating image and multi-round dialogue OOD detection is more effective with previously unseen labels than using either modality independently. In the presence of mismatched pairs, our proposed score effectively identifies these mismatches and demonstrates strong robustness in long dialogues. This approach enhances domain-aware, adaptive conversational agents and establishes baselines for future studies.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

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Submission Number: 10058