Fine-Tuning Multimodal Models for Zero-Shot XTREME xGQA Performance

Intermediate-task training---fine-tuning a pretrained model on an intermediate task before fine-tuning again on the target task---often improves model performance substantially on language understanding tasks in monolingual English settings. We investigate whether English intermediate-task training is still helpful on non-English target tasks. Using nine intermediate language-understanding tasks, we evaluate intermediate-task transfer in a zero-shot cross-lingual setting on the XTREME benchmark. We see large improvements from intermediate training on the BUCC and Tatoeba sentence retrieval tas

Research goal: Does fine-tuning large multimodal models on English intermediate tasks like visual question answering (VQA) or image captioning improve zero-shot performance on XTREME xGQA compared to text-only intermediate tasks, as measured by accuracy and inference latency?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.0/10.