Recently, very large language models (LLMs) have shown exceptional
performance on several English NLP tasks with just in-context learning (ICL),
but their utility in other languages is still underexplored. We investigate
their effectiveness for NLP tasks in low-resource languages (LRLs), especially
in the setting of zero-labelled cross-lingual transfer (0-CLT), where no
labelled training data for the target language is available -- however training
data from one or more related medium-resource languages (MRLs) is utilized,
alongside the available unlabeled test data for a target language. We introduce
Self-Supervised Prompting (SSP), a novel ICL approach tailored for the 0-CLT
setting.
SSP is based on the key observation that LLMs output more accurate labels if
in-context exemplars are from the target language (even if their labels are
slightly noisy). To operationalize this, since target language training data is
not available in 0-CLT, SSP operates in two stages. In Stage I, using source
MRL training data, target language's test data is noisily labeled. In Stage II,
these noisy test data points are used as exemplars in ICL for further improved
labelling. Additionally, our implementation of SSP uses a novel Integer Linear
Programming (ILP)-based exemplar selection that balances similarity, prediction
confidence (when available) and label coverage. Experiments on three tasks and
eleven LRLs (from three regions) demonstrate that SSP strongly outperforms
existing SOTA fine-tuned and prompting-based baselines in 0-CLT setup.

在这项研究中，我们探讨了在语言资源有限情况下，特别是零标记的跨语言转移（0-CLT）环境下，基于自我监督提示（SSP）的大型语言模型（LLM）在自然语言处理（NLP）任务中的效果，并通过实验证明 SSP 在这个设置中优于现有的基线模型。