‘No’ Matters: Out-of-Distribution Detection in Multimodality Multi-Turn Interactive Dialogue Download PDF

Out-of-distribution (OOD) detection in multimodal contexts is essential for identifying deviations in different modalities, particularly for interactive dialogue systems in real-life interactions, where the systems are usually infeasible to deploy large language models (LLMs) to generate responses due to data privacy and ethical issues.This paper aims to improve label detection that involves multiround long dialogues by efficiently detecting OOD dialogues and images.We introduce a novel scoring framework named Dialogue Image Aligning and Enhancing Framework (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, with user case as evidence to demonstrate the effectiveness of proposed function for applications.This approach enhances domain-aware, adaptive conversational agents and establishes baselines for future studies.