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Multilingual image captioning has recently been tackled by training withlarge-scale machine translated data, which is an expensive, noisy, andtime-consuming process. Without requiring any multilingual caption data, wepropose LMCap, an image-blind few-shot multilingual captioning model that worksby prompting a language model with retrieved captions. Specifically, instead offollowing the standard encoder-decoder paradigm, given an image, LMCap firstretrieves the captions of similar images using a multilingual CLIP encoder.These captions are then combined into a prompt for an XGLM decoder, in order togenerate captions in the desired language. In other words, the generation modeldoes not directly process the image, instead processing retrieved captions.Experiments on the XM3600 dataset of geographically diverse images show thatour model is competitive with fully-supervised multilingual captioning models,without requiring any supervised training on any captioning data.

LMCap: Few-shot Multilingual Image Captioning by Retrieval Augmented Language Model Prompting