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        <identifier>oai:figshare.com:article/30338983</identifier>
        <datestamp>2025-10-13T14:11:01Z</datestamp>
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          <dc:title>Prompt engineering on large language models (LLMs) in low-resourced language setting</dc:title>
          <dc:creator>Pitso Khoboko (22359745)</dc:creator>
          <dc:subject>Natural language processing</dc:subject>
          <dc:subject>Data engineering and data science</dc:subject>
          <dc:subject>Translation</dc:subject>
          <dc:subject>Prompt engineering</dc:subject>
          <dc:subject>Low-resourced languages</dc:subject>
          <dc:subject>Large language models (LLMs)</dc:subject>
          <dc:subject>In context learning</dc:subject>
          <dc:subject>SDG 4 Quality education</dc:subject>
          <dc:subject>SDG 10 Reduced inequalities</dc:subject>
          <dc:description>&lt;p dir="ltr"&gt;The attached dataset has all the information in regard to Large Language Models (LLMs) being used in low-resource language settings. There are figures depicting the prompt structure used to improve translation of LLMs translation of low-resourced languages, more specifically, low-resourced, morphologically rich African languages. Furthermore, there are figures that depict the results of various experiments conducted in efforts to improve LLMs' translation of low-resourced languages, and there are clean evaluation datasets that can be used by other researchers to evaluate their LLMs in low-resourced language settings.&lt;/p&gt;</dc:description>
          <dc:date>2025-10-13T14:03:36Z</dc:date>
          <dc:type>Dataset</dc:type>
          <dc:type>Dataset</dc:type>
          <dc:identifier>10.25403/UPresearchdata.30338983.v2</dc:identifier>
          <dc:relation>https://figshare.com/articles/dataset/Prompt_engineering_on_large_language_models_LLMs_in_low-resourced_language_setting/30338983</dc:relation>
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