Empowering Cross-lingual Abilities of Instruction-tuned Large Language
  Models by Translation-following demonstrations
Leonardo Ranaldi Giulia Pucci Andre Freitas
The language ability of Large Language Models (LLMs) is often unbalanced
towards English because of the imbalance in the distribution of the
pre-training data. This disparity is demanded in further fine-tuning and
affecting the cross-lingual abilities of LLMs. In this paper, we propose to
empower Instructiontuned LLMs (It-LLMs) in languages other than English by
building semantic alignment between them. Hence, we propose CrossAlpaca, an
It-LLM with cross-lingual instruction-following and Translation-following
demonstrations to improve semantic alignment between languages. We validate our
approach on the multilingual Question Answering (QA) benchmarks XQUAD and MLQA
and adapted versions of MMLU and BBH. Our models, tested over six different
languages, outperform the It-LLMs tuned on monolingual data. The final results
show that instruction tuning on non-English data is not enough and that
semantic alignment can be further improved by Translation-following
demonstrations.