Collection of the most awesome ChatGPT prompts found on the internet. We have 150+ prompts we have sourced and also have a place for you to submit your own to our community! You can easily find a prompt across different categories, copy to your clipboard, and run it quickly in ChatGPT.
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Oh, ChatGPT! Some 2 months on the market and a not so tiny ecosystem has developed all on its own, with lists of prompts, tips, APIs, use cases, extensions, success stories and failures. ChatGPT is the first true foundation model for the mass-market.
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PDF | We investigate the extent to which modern neural language models are susceptible to structural priming, the phenomenon whereby the structure of a... | Find, read and cite all the research you need on ResearchGate
This work shows that finetuning LMs in the few-shot setting can considerably reduce the need for prompt engineering, and recommends finetuned LMs for few- shot learning as it is more accurate, robust to different prompts, and can be made nearly as efficient as using frozen LMs. Prompting language models (LMs) with training examples and task descriptions has been seen as critical to recent successes in few-shot learning. In this work, we show that finetuning LMs in the few-shot setting can considerably reduce the need for prompt engineering. In fact, one can use null prompts, prompts that contain neither task-specific templates nor training examples, and achieve competitive accuracy to manually-tuned prompts across a wide range of tasks. While finetuning LMs does introduce new parameters for each downstream task, we show that this memory overhead can be substantially reduced: finetuning only the bias terms can achieve comparable or better accuracy than standard finetuning while only updating 0.1% of the parameters. All in all, we recommend finetuning LMs for few-shot learning as it is more accurate, robust to different prompts, and can be made nearly as efficient as using frozen LMs.
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