Multimodal Intermediate Task Effects on Cross-Lingual Zero-Shot Performance in XTREME-R

Abstract

Abstract: Instruction tuning (IT) is widely used to teach pretrained large language models (LLMs) to follow arbitrary instructions, but is under-studied in multilingual settings. In this work, we conduct a systematic study of zero-shot cross-lingual transfer in IT, when an LLM is instruction-tuned on English-only data and then tested on user prompts in other languages. We advocate for the importance of evaluating various aspects of model responses in multilingual instruction following and investigate the influence of different model configuration choices. We find that cross-lingual transfer does happen

Research Question

How do multimodal intermediate tasks (e.g., instruction-following with image-text pairs) influence zero-shot cross-lingual transfer performance on XTREME-R compared to text-only instruction-tuned models, as measured by accuracy and reasoning alignment scores?

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