'title': 'Co-training improves prompt-based learning for large language '               'models',      'link': 'https://proceedings.mlr.press/v162/lang22a.html',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': '‚Ä¶ With the success of large language models (LLMs) of code and '                 'their use as ‚Ä¶ prompt design process become important. In this '                 'work, we propose a framework called Repo-Level Prompt ‚Ä¶',      'title': 'Repository-level prompt generation for large language models of '               'code',      'link': 'https://arxiv.org/abs/2206.12839',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': '‚Ä¶ Figure 2 | The benefits of different components of a prompt '                 'for the largest language model (Gopher), as estimated from '                 'hierarchical logistic regression. Each point estimates the '                 'unique ‚Ä¶',      'title': 'Can language models learn from explanations in context?',      'link': 'https://arxiv.org/abs/2204.02329',      'engines': ['google scholar'],      'category': 'science'}]Get papers from arxivresults = 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 '
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. ->: 'title': 'Co-training improves prompt-based learning for large language '               'models',      'link': 'https://proceedings.mlr.press/v162/lang22a.html',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': '‚Ä¶ With the success of large language models (LLMs) of code and '                 'their use as ‚Ä¶ prompt design process become important. In this '                 'work, we propose a framework called Repo-Level Prompt ‚Ä¶',      'title': 'Repository-level prompt generation for large language models of '               'code',      'link': 'https://arxiv.org/abs/2206.12839',      'engines': ['google scholar'],      'category': 'science'},     {'snippet': '‚Ä¶ Figure 2 | The benefits of different components of a prompt '                 'for the largest language model (Gopher), as estimated from '                 'hierarchical logistic regression. Each point estimates the '                 'unique ‚Ä¶',      'title': 'Can language models learn from explanations in context?',      'link': 'https://arxiv.org/abs/2204.02329',      'engines': ['google scholar'],      'category': 'science'}]Get papers from arxivresults = 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 '