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Found 4 papers, 1 papers with code
Ethical Considerations for Low-resourced Machine Translation
This paper considers some ethical implications of machine translation for low-resourced languages.
Improving Cross-Domain Low-Resource Text Generation through LLM Post-Editing: A Programmer-Interpreter Approach
Post-editing has proven effective in improving the quality of text generated by large language models (LLMs) such as GPT-3. 5 or GPT-4, particularly when direct updating of their parameters to enhance text quality is infeasible or expensive.
Reranking for Natural Language Generation from Logical Forms: A Study based on Large Language Models
Our approach involves initially generating a set of candidate outputs by prompting an LLM and subsequently reranking them using a task-specific reranker model.
Multilingual unsupervised sequence segmentation transfers to extremely low-resource languages
Further, we show that this transfer can be achieved by training over a collection of low-resource languages that are typologically similar (but phylogenetically unrelated) to the target language.
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