Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders

TL;DR

This paper investigates cross-linguistic disparities in multilingual LLMs using Sparse Autoencoders, revealing activation gaps in low-resource languages and demonstrating that activation-aware fine-tuning improves performance while maintaining English proficiency.

Contribution

It introduces a novel analysis of activation patterns across languages and proposes activation-aware fine-tuning with LoRA to reduce disparities in multilingual LLMs.

Findings

Medium to low resource languages have up to 26.27% lower activations in early layers.

Activation-aware fine-tuning significantly increases activation levels in low-resource languages.

Benchmark performance improves modestly after activation alignment, with maintained English performance.

Abstract

Multilingual large language models (LLMs) exhibit strong cross-linguistic generalization, yet medium to low resource languages underperform on common benchmarks such as ARC-Challenge, MMLU, and HellaSwag. We analyze activation patterns in Gemma-2-2B across all 26 residual layers and 10 languages: Chinese (zh), Russian (ru), Spanish (es), Italian (it), medium to low resource languages including Indonesian (id), Catalan (ca), Marathi (mr), Malayalam (ml), and Hindi (hi), with English (en) as the reference. Using Sparse Autoencoders (SAEs), we reveal systematic disparities in activation patterns. Medium to low resource languages receive up to 26.27 percent lower activations in early layers, with a persistent gap of 19.89 percent in deeper layers. To address this, we apply activation-aware fine-tuning via Low-Rank Adaptation (LoRA), leading to substantial activation gains, such as 87.69 percent for Malayalam and 86.32 percent for Hindi, while maintaining English retention at approximately 91 percent. After fine-tuning, benchmark results show modest but consistent improvements, highlighting activation alignment as a key factor in enhancing multilingual LLM performance.