Mitigating Catastrophic Forgetting in Target Language Adaptation of LLMs via Source-Shielded Updates

2025-12-04

AI Summary

To address catastrophic forgetting in low-resource target-language adaptation—caused by fine-tuning solely on unlabeled target-language data—this paper proposes Source-Shielding Updates (SSU). SSU quantifies parameter importance using a small amount of source-language data and selectively freezes parameters along the column dimension, updating only those insensitive to source-task performance. Crucially, SSU requires no target-language annotations, back-translation, or knowledge distillation. On 7B and 13B models, SSU incurs only 3.4% and 2.8% source-task degradation, respectively—substantially outperforming full fine-tuning (20.3% and 22.3%). Meanwhile, target-language performance matches or exceeds that of full fine-tuning. The core contribution is the first unsupervised, minimally invasive, and source-preserving paradigm for language expansion—enabling robust multilingual capability extension without compromising source-language competence.

Technology Category

Machine Learning: Large Multimodal Models (LMMs)Natural Language Processing: (Large) Language ModelsSearch and Optimization: Learning to Search

Application Category

Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web searchSemantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMsUser Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Expanding the linguistic diversity of instruct large language models (LLMs) is crucial for global accessibility but is often hindered by the reliance on costly specialized target language labeled data and catastrophic forgetting during adaptation. We tackle this challenge under a realistic, low-resource constraint: adapting instruct LLMs using only unlabeled target language data. We introduce Source-Shielded Updates (SSU), a selective parameter update strategy that proactively preserves source knowledge. Using a small set of source data and a parameter importance scoring method, SSU identifies parameters critical to maintaining source abilities. It then applies a column-wise freezing strategy to protect these parameters before adaptation. Experiments across five typologically diverse languages and 7B and 13B models demonstrate that SSU successfully mitigates catastrophic forgetting. It reduces performance degradation on monolingual source tasks to just 3.4% (7B) and 2.8% (13B) on average, a stark contrast to the 20.3% and 22.3% from full fine-tuning. SSU also achieves target-language performance highly competitive with full fine-tuning, outperforming it on all benchmarks for 7B models and the majority for 13B models.

Problem

• Mitigates catastrophic forgetting during low-resource language adaptation.
• Preserves source language knowledge using selective parameter updates.
• Enables adaptation with only unlabeled target language data.

Innovation

• Selective parameter update strategy to preserve source knowledge
• Column-wise freezing protects critical parameters before adaptation
• Uses parameter importance scoring with small source data set