Uncovering Cross-Linguistic Disparities in LLMs using Sparse Autoencoders
This tackles performance disparities for medium to low resource languages in multilingual LLMs, though it is incremental as it builds on existing methods like SAEs and LoRA.
The paper analyzed activation patterns in multilingual LLMs using Sparse Autoencoders, finding that medium to low resource languages had up to 26.27% lower activations than English, and addressed this with activation-aware fine-tuning via LoRA, achieving gains like 87.69% for Malayalam while retaining 91% English performance.
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.