Can you map it to English? The Role of Cross-Lingual Alignment in Multilingual Performance of LLMs

Understand

Large language models (LLMs) can answer prompts in many languages, despite being trained predominantly on English; yet, the mechanisms driving this generalization remain poorly understood.

• This work asks: How does an LLM's ability to align representations of non-English inputs to English impact its performance on natural language understanding (NLU) tasks? We study the role of representation alignment in instance-level task decisions, complementing prior analyses conducted both at the language level and task-independently.
• We introduce the Discriminative Alignment Index ($\DALI$) to quantify instance-level alignment across 24 languages other than English and three distinct NLU tasks.
• Results show that incorrect NLU predictions are strongly associated with lower representation alignment with English in the model's middle layers.