
doi: 10.5281/zenodo.21552989 , 10.5281/zenodo.21240901 , 10.5281/zenodo.21464983 , 10.5281/zenodo.21315953 , 10.5281/zenodo.20789367 , 10.5281/zenodo.21516339 , 10.5281/zenodo.21516338 , 10.5281/zenodo.20789366 , 10.5281/zenodo.21424723 , 10.5281/zenodo.21552990 , 10.5281/zenodo.21597460 , 10.5281/zenodo.21240902 , 10.5281/zenodo.21469604 , 10.5281/zenodo.21256679 , 10.5281/zenodo.21424722 , 10.5281/zenodo.21535274 , 10.5281/zenodo.21310944 , 10.5281/zenodo.21567410 , 10.5281/zenodo.21398718 , 10.5281/zenodo.21232430 , 10.5281/zenodo.21315954 , 10.5281/zenodo.21541070 , 10.5281/zenodo.21376998 , 10.5281/zenodo.21471204 , 10.5281/zenodo.21464982 , 10.5281/zenodo.21398717 , 10.5281/zenodo.21232429 , 10.5281/zenodo.21535275 , 10.5281/zenodo.21312862 , 10.5281/zenodo.21469603 , 10.5281/zenodo.21312863 , 10.5281/zenodo.21310943 , 10.5281/zenodo.21471205 , 10.5281/zenodo.21256680 , 10.5281/zenodo.21567411 , 10.5281/zenodo.21541071 , 10.5281/zenodo.21597459 , 10.5281/zenodo.21376997
doi: 10.5281/zenodo.21552989 , 10.5281/zenodo.21240901 , 10.5281/zenodo.21464983 , 10.5281/zenodo.21315953 , 10.5281/zenodo.20789367 , 10.5281/zenodo.21516339 , 10.5281/zenodo.21516338 , 10.5281/zenodo.20789366 , 10.5281/zenodo.21424723 , 10.5281/zenodo.21552990 , 10.5281/zenodo.21597460 , 10.5281/zenodo.21240902 , 10.5281/zenodo.21469604 , 10.5281/zenodo.21256679 , 10.5281/zenodo.21424722 , 10.5281/zenodo.21535274 , 10.5281/zenodo.21310944 , 10.5281/zenodo.21567410 , 10.5281/zenodo.21398718 , 10.5281/zenodo.21232430 , 10.5281/zenodo.21315954 , 10.5281/zenodo.21541070 , 10.5281/zenodo.21376998 , 10.5281/zenodo.21471204 , 10.5281/zenodo.21464982 , 10.5281/zenodo.21398717 , 10.5281/zenodo.21232429 , 10.5281/zenodo.21535275 , 10.5281/zenodo.21312862 , 10.5281/zenodo.21469603 , 10.5281/zenodo.21312863 , 10.5281/zenodo.21310943 , 10.5281/zenodo.21471205 , 10.5281/zenodo.21256680 , 10.5281/zenodo.21567411 , 10.5281/zenodo.21541071 , 10.5281/zenodo.21597459 , 10.5281/zenodo.21376997
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: Do multimodal intermediate tasks (e.g., image-text alignment) improve zero-shot cross-lingual transfer performance on XTREME-R compared to text-only intermediate tasks? Autonomous synthesis report generated by Assignee Research. Tribunal consensus score: 9.3/10.
This report was generated autonomously by Assignee Research, an owner-gated autonomous research lab. The content synthesizes findings from peer-reviewed papers. Tribunal score: 9.3/10.
alongside, further, zero-shot, image-text, intermediate, combining, image, tasks, question, retrieval, answering, vision-language, captioning, adding, multimodal, CLIP, language-understanding, alignment, improve, inclusion, VLM-based, incorporating, impact, cross-lingual, visual, use, transfer, image-captioning, performance
alongside, further, zero-shot, image-text, intermediate, combining, image, tasks, question, retrieval, answering, vision-language, captioning, adding, multimodal, CLIP, language-understanding, alignment, improve, inclusion, VLM-based, incorporating, impact, cross-lingual, visual, use, transfer, image-captioning, performance
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
