用相关语言的例句库提升低资源印地语的少样本学习效果
PromptRefine: Enhancing Few-Shot Performance on Low-Resource Indic Languages with Example Selection from Related Example Banks
- 通过交替优化方法从高资源印地语例句库中筛选最佳示例
- 在4个文本生成任务上显著提升大模型性能，最高提升12.3%
- 适合需要跨语言少样本学习的研究者和开发者
大型语言模型（LLMs）在少样本学习中展现出强大能力，但其表现高度依赖示范样本的选择。这一问题在低资源印地语中尤为突出，因真实数据稀缺导致选择困难。本文提出PromptRefine，一种基于交替最小化的示例选择新方法，利用相关高资源印地语例句库，结合多任务学习对齐语言特定检索器，实现跨语言有效检索。同时引入多样性机制，提升泛化能力并减少偏差。在跨语言问答、多语言问答、机器翻译与跨语言摘要四个任务上，使用LLAMA-3.1-8B、LLAMA-2-7B、Qwen-2-7B及Qwen-2.5-7B等先进模型进行评估，结果表明PromptRefine显著优于现有框架。
原文摘要 · Abstract (English)
Large Language Models (LLMs) have recently demonstrated impressive few-shot learning capabilities through in-context learning (ICL). However, ICL performance is highly dependent on the choice of few-shot demonstrations, making the selection of the most optimal examples a persistent research challenge. This issue is further amplified in low-resource Indic languages, where the scarcity of ground-truth data complicates the selection process. In this work, we propose PromptRefine, a novel Alternating Minimization approach for example selection that improves ICL performance on low-resource Indic languages. PromptRefine leverages auxiliary example banks from related high-resource Indic languages and employs multi-task learning techniques to align language-specific retrievers, enabling effective cross-language retrieval. Additionally, we incorporate diversity in the selected examples to enhance generalization and reduce bias. Through comprehensive evaluations on four text generation tasks -- Cross-Lingual Question Answering, Multilingual Question Answering, Machine Translation, and Cross-Lingual Summarization using state-of-the-art LLMs such as LLAMA-3.1-8B, LLAMA-2-7B, Qwen-2-7B, and Qwen-2.5-7B, we demonstrate that PromptRefine significantly outperforms existing frameworks for retrieving examples.
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