Like a bilingual baby: The advantage of visually grounding a bilingual language model

Abstract: Unlike most neural LLMs, humans learn language in a rich, multi-sensory and, often, multi-lingual environment. Current LLMs typically fail to fully capture the complexities of multilingual language use. We train an LSTM LLM on images and captions in English and Spanish from MS-COCO-ES. We find that the visual grounding improves the model's understanding of semantic similarity both within and across languages and improves perplexity. However, we find no significant advantage of visual grounding for abstract words. Our results provide additional evidence of the advantages of visually grounded LLMs and point to the need for more naturalistic language data from multilingual speakers and multilingual datasets with perceptual grounding.