Le concepteur de ChatGPT étudie plusieurs options, dont celle d’une potentielle acquisition qui lui permettrait de ne pas partir de...-Intelligence artificielle

Explore developer resources, tutorials, API docs, and dynamic examples to get the most out of OpenAI's platform.

OpenAI is a well trodden blog and YouTube path - in this post, I’m going to cover the idea of using it to gather information. Let’s say, for the sake of…

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Machine learning is one way of achieving artificial intelligence. This guide is a comprehensive look at the foundations and applications of machine learning.

Learn how to sign up for an OpenAI account and retrieve the API key of the OpenAI API.

Natural language processing enables machines to understand and respond to text or voice data.

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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.

How to Fine-Tune OpenAI models using W&B.

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Visualizing the relationship between COVID-19 vaccine adoption and online (mis)information. From IU Observatory on Social Media (OSoMe).

Interactive Visualization of Twitter Information

Analyze volume of tweets over given period of time

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