'link': 'https://arxiv.org/abs/2204.02329',
  'engines': ['google scholar'],
  'category': 'science'}]
Get papers from arxiv
results = search.results("Large Language Model prompt", num_results=5, engines=['arxiv'])
pprint.pp(results)
[{'snippet': 'Thanks to the advanced improvement of large pre-trained language '
             'models, prompt-based fine-tuning is shown to be effective on a '
             'variety of downstream tasks. Though many prompting methods have '
             'been investigated, it remains unknown which type of prompts are '
             'the most effective among three types of prompts (i.e., '
             'human-designed prompts, schema prompts and null prompts). In '
             'this work, we empirically compare the three types of prompts '
             'under both few-shot and fully-supervised settings. Our '
             'experimental results show that schema prompts are the most '
             'effective in general. Besides, the performance gaps tend to '
             'diminish when the scale of training data grows large.',
  'title': 'Do Prompts Solve NLP Tasks Using Natural Language?',
  'link': 'http://arxiv.org/abs/2203.00902v1',
  'engines': ['arxiv'],
  'category': 'science'},
 {'snippet': 'Cross-prompt automated essay scoring (AES) requires the system '
             'to use non target-prompt essays to award scores to a '
             'target-prompt essay. Since obtaining a large quantity of '
             'pre-graded essays to a particular prompt is often difficult and '
             'unrealistic, the task of cross-prompt AES is vital for the '
             'development of real-world AES systems, yet it remains an '
rtdocs_stable/api.python.langchain.com/en/stable/modules/agents/tools/examples/searx_search.html