Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem
Abstract: 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 information 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 prompt type, retrieval method, model type, and language community-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.
Paper Type: Long
Research Area: Efficient/Low-Resource Methods for NLP
Research Area Keywords: NLP in resource-constrained settings, ethical considerations in NLP applications, human evaluation, participatory/community-based NLP, few-shot/zero-shot MT, less-resourced languages, indigenous languages, minoritized languages
Contribution Types: Model analysis & interpretability, Approaches to low-resource settings, Data analysis
Languages Studied: Southern Quechua, Spanish, English
Submission Number: 3531
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