CLIP-Based Model Inference Efficiency in Zero-Shot Cross-Lingual Image Retrieval After Intermediate-Task Training on English VQA

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: What is the impact of intermediate-task training on English VQA datasets on the inference efficiency (e.g., latency and throughput) of CLIP-based models when performing zero-shot cross-lingual image retrieval on the XTREME-R benchmark?

Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 8.5/10.