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In this study, we use the existing Large Language Models ENnhanced to SeeFramework (LENS Framework) to test the feasibility of multimodal task-orienteddialogues. The LENS Framework has been proposed as a method to solve computervision tasks without additional training and with fixed parameters ofpre-trained models. We used the Multimodal Dialogs (MMD) dataset, a multimodaltask-oriented dialogue benchmark dataset from the fashion field, and for theevaluation, we used the ChatGPT-based G-EVAL, which only accepts textualmodalities, with arrangements to handle multimodal data. Compared toTransformer-based models in previous studies, our method demonstrated anabsolute lift of 10.8% in fluency, 8.8% in usefulness, and 5.2% in relevanceand coherence. The results show that using large-scale models with fixedparameters rather than using models trained on a dataset from scratch improvesperformance in multimodal task-oriented dialogues. At the same time, we showthat Large Language Models (LLMs) are effective for multimodal task-orienteddialogues. This is expected to lead to efficient applications to existingsystems.

Application of frozen large-scale models to multimodal task-oriented dialogue