Summary of Shortcomings Of Llms For Low-resource Translation: Retrieval and Understanding Are Both the Problem, by Sara Court and Micha Elsner
Shortcomings of LLMs for Low-Resource Translation: Retrieval and Understanding are Both the Problem
by Sara Court, Micha Elsner
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary Medium Difficulty summary: This paper explores the capabilities of large language models (LLMs) in translating text from low-resource languages into high-resource languages. Specifically, it investigates the performance of LLMs when instructed to translate Southern Quechua into Spanish as part of a machine translation pipeline. The study examines the effectiveness of various types of context, including dictionaries, grammar lessons, and parallel corpora, in improving the accuracy of model output. Using both automatic and human evaluation methods, the research conducts ablation studies that manipulate factors such as context type, retrieval method, and model type. The results show that even smaller LLMs can leverage prompt context for zero-shot low-resource translation when provided minimal linguistic information. However, the study also highlights the limitations of using even top-performing LLMs as translation systems for most of the world’s languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: This research looks at how well large language models do in translating text from one language to another. They tested these models by asking them to translate Southern Quechua into Spanish. The study found that giving the models more information about grammar, dictionaries, and examples can help improve their translations. However, even with this extra help, the models aren’t good enough to be used for most languages. This is because each language has its own unique characteristics and quirks. |
Keywords
* Artificial intelligence * Prompt * Translation * Zero shot
