'engines': ['arxiv'],      'category': 'science'},     {'snippet': 'Large language models can perform new tasks in a zero-shot '                 'fashion, given natural language prompts that specify the desired '                 'behavior. Such prompts are typically hand engineered, but can '                 'also be learned with gradient-based methods from labeled data. '                 'However, it is underexplored what factors make the prompts '                 'effective, especially when the prompts are natural language. In '                 'this paper, we investigate common attributes shared by effective '                 'prompts. We first propose a human readable prompt tuning method '                 '(F LUENT P ROMPT) based on Langevin dynamics that incorporates a '                 'fluency constraint to find a diverse distribution of effective '                 'and fluent prompts. Our analysis reveals that effective prompts '                 'are topically related to the task domain and calibrate the prior '                 'probability of label words. Based on these findings, we also '                 'propose a method for generating prompts using only unlabeled '                 'data, outperforming strong baselines by an average of 7.0% '                 'accuracy across three tasks.',      'title': "Toward Human Readable Prompt Tuning: Kubrick's The Shining is a "               'good movie, and a good prompt too?',      'link': 'http://arxiv.org/abs/2212.10539v1',      'engines': ['arxiv'],      'category': 'science'},     {'snippet': 'Prevailing methods for mapping large generative language models '                 "to supervised tasks may fail to sufficiently probe models' novel "                 'capabilities. Using GPT-3 as a case study, we show that 0-shot '                 'prompts can significantly outperform few-shot prompts. We '                 'suggest that the function of few-shot examples in these cases is '
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. ->: 'engines': ['arxiv'],      'category': 'science'},     {'snippet': 'Large language models can perform new tasks in a zero-shot '                 'fashion, given natural language prompts that specify the desired '                 'behavior. Such prompts are typically hand engineered, but can '                 'also be learned with gradient-based methods from labeled data. '                 'However, it is underexplored what factors make the prompts '                 'effective, especially when the prompts are natural language. In '                 'this paper, we investigate common attributes shared by effective '                 'prompts. We first propose a human readable prompt tuning method '                 '(F LUENT P ROMPT) based on Langevin dynamics that incorporates a '                 'fluency constraint to find a diverse distribution of effective '                 'and fluent prompts. Our analysis reveals that effective prompts '                 'are topically related to the task domain and calibrate the prior '                 'probability of label words. Based on these findings, we also '                 'propose a method for generating prompts using only unlabeled '                 'data, outperforming strong baselines by an average of 7.0% '                 'accuracy across three tasks.',      'title': "Toward Human Readable Prompt Tuning: Kubrick's The Shining is a "               'good movie, and a good prompt too?',      'link': 'http://arxiv.org/abs/2212.10539v1',      'engines': ['arxiv'],      'category': 'science'},     {'snippet': 'Prevailing methods for mapping large generative language models '                 "to supervised tasks may fail to sufficiently probe models' novel "                 'capabilities. Using GPT-3 as a case study, we show that 0-shot '                 'prompts can significantly outperform few-shot prompts. We '                 'suggest that the function of few-shot examples in these cases is '