---
id: 20260717-T0-17
title: "元学习偏好：一种低资源语言的多语言LLM对齐方法"
title_en: "Meta-Learning Preferences for Multilingual LLM Alignment in Low-Resource Languages"
url: https://ai.daily.yangsir.net/daily/20260717-T0-17
issue_date: 2026-07-17
publish_date: 2026-07-16T04:00:00.000Z
category: research
source_name: "arXiv cs.CL (NLP)"
source_url: https://arxiv.org/abs/2607.13315
---

# 元学习偏好：一种低资源语言的多语言LLM对齐方法

新论文提出一种元学习方法，解决多语言大模型对齐中低资源语言缺乏人类偏好数据的问题。该方法通过从高资源语言中学习偏好元知识，再迁移到低资源语言，显著提升对齐效果，而不依赖大量低资源语言标注数据。

## English Version

**Meta-Learning Preferences for Multilingual LLM Alignment in Low-Resource Languages**

A new paper proposes a meta-learning approach to align multilingual LLMs where low-resource languages lack human preference data. It learns preference meta-knowledge from high-resource languages and transfers it to low-resource ones, significantly improving alignment without requiring extensive labeled data.

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**来源**：[arXiv cs.CL (NLP)](https://arxiv.org/abs/2607.13315)

**详情页**：https://ai.daily.yangsir.net/daily/20260717-T0-17

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