Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem
It addresses the problem of low-resource machine translation for speakers of underrepresented languages, but the findings are incremental as they reveal limitations in existing LLM approaches.
This work investigates the in-context learning abilities of LLMs for translating Southern Quechua to Spanish, finding that small LLMs can perform zero-shot translation with relevant linguistic context, but context type, retrieval method, and model type affect performance, highlighting limitations for most of the world's 7,000+ languages.
This work investigates the in-context learning abilities of pretrained large language models (LLMs) when instructed to translate text from a low-resource language into a high-resource language as part of an automated machine translation pipeline. We conduct a set of experiments translating Southern Quechua to Spanish and examine the informativity of various types of context retrieved from a constrained database of digitized pedagogical materials (dictionaries and grammar lessons) and parallel corpora. Using both automatic and human evaluation of model output, we conduct ablation studies that manipulate (1) context type (morpheme translations, grammar descriptions, and corpus examples), (2) retrieval methods (automated vs. manual), and (3) model type. Our results suggest that even relatively small LLMs are capable of utilizing prompt context for zero-shot low-resource translation when provided a minimally sufficient amount of relevant linguistic information. However, the variable effects of context type, retrieval method, model type, and language-specific factors highlight the limitations of using even the best LLMs as translation systems for the majority of the world's 7,000+ languages and their speakers.