SSP: Self-Supervised Prompting for Cross-Lingual Transfer to Low-Resource Languages using Large Language Models

TL;DR

This paper introduces Self-Supervised Prompting (SSP), a novel zero-shot cross-lingual transfer method for large language models that improves label accuracy in low-resource languages by leveraging noisy exemplars and ILP-based selection.

Contribution

The paper proposes SSP, a new two-stage prompting approach with ILP-based exemplar selection, specifically designed for zero-label cross-lingual transfer in low-resource languages using large language models.

Findings

SSP outperforms existing baselines on multiple tasks and languages.

SSP effectively leverages noisy exemplars for improved label accuracy.

The method demonstrates strong results across diverse low-resource languages.

Abstract

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.