Automated Essay Scoring',      'link': 'http://arxiv.org/abs/2008.01441v1',      'engines': ['arxiv'],      'category': 'science'},     {'snippet': 'Research on prompting has shown excellent performance with '                 'little or even no supervised training across many tasks. '                 'However, prompting for machine translation is still '                 'under-explored in the literature. We fill this gap by offering a '                 'systematic study on prompting strategies for translation, '                 'examining various factors for prompt template and demonstration '                 'example selection. We further explore the use of monolingual '                 'data and the feasibility of cross-lingual, cross-domain, and '                 'sentence-to-document transfer learning in prompting. Extensive '                 'experiments with GLM-130B (Zeng et al., 2022) as the testbed '                 'show that 1) the number and the quality of prompt examples '                 'matter, where using suboptimal examples degenerates translation; '                 '2) several features of prompt examples, such as semantic '                 'similarity, show significant Spearman correlation with their '                 'prompting performance; yet, none of the correlations are strong '                 'enough; 3) using pseudo parallel prompt examples constructed '                 'from monolingual data via zero-shot prompting could improve '                 'translation; and 4) improved performance is achievable by '                 'transferring knowledge from prompt examples selected in other '                 'settings. We finally provide an analysis on the model outputs '                 'and discuss several problems that prompting still suffers from.',      'title': 'Prompting Large Language Model for Machine Translation: A Case '               'Study',      'link': 'http://arxiv.org/abs/2301.07069v2',      'engines': ['arxiv'],
This notebook goes over how to use a self hosted SearxNG search API to search the web.
This notebook goes over how to use a self hosted SearxNG search API to search the web. ->: Automated Essay Scoring',      'link': 'http://arxiv.org/abs/2008.01441v1',      'engines': ['arxiv'],      'category': 'science'},     {'snippet': 'Research on prompting has shown excellent performance with '                 'little or even no supervised training across many tasks. '                 'However, prompting for machine translation is still '                 'under-explored in the literature. We fill this gap by offering a '                 'systematic study on prompting strategies for translation, '                 'examining various factors for prompt template and demonstration '                 'example selection. We further explore the use of monolingual '                 'data and the feasibility of cross-lingual, cross-domain, and '                 'sentence-to-document transfer learning in prompting. Extensive '                 'experiments with GLM-130B (Zeng et al., 2022) as the testbed '                 'show that 1) the number and the quality of prompt examples '                 'matter, where using suboptimal examples degenerates translation; '                 '2) several features of prompt examples, such as semantic '                 'similarity, show significant Spearman correlation with their '                 'prompting performance; yet, none of the correlations are strong '                 'enough; 3) using pseudo parallel prompt examples constructed '                 'from monolingual data via zero-shot prompting could improve '                 'translation; and 4) improved performance is achievable by '                 'transferring knowledge from prompt examples selected in other '                 'settings. We finally provide an analysis on the model outputs '                 'and discuss several problems that prompting still suffers from.',      'title': 'Prompting Large Language Model for Machine Translation: A Case '               'Study',      'link': 'http://arxiv.org/abs/2301.07069v2',      'engines': ['arxiv'],