Language

Meet Alpaca: Stanford University’s Instruction-Following Language Model that Matches GPT-3.5 Performance Alpaca in Motion

The model relies on Meta AI’s LLaMA and stays significatively smaller than GPT-3.5.Despite the impressive capabilities of Alpaca, the model still exhibits among the classic limitations of instruction following models akin to toxicity, hallucinations...

Language models might have the option to self-correct biases—for those who ask them

The second test used a knowledge set designed to ascertain how likely a model is to assume the gender of somebody in a selected career, and the third tested for the way much...

An early have a look at the labor market impact potential of huge language models

We investigate the potential implications of Generative Pre-trained Transformer (GPT) models and related technologies on the U.S. labor market. Using a latest rubric, we assess occupations based on their correspondence with GPT capabilities, incorporating...

GPT-4 is Here: Is It Really Changing the Game for Language AI? On How Things Progress Winter Is Not Coming Conclusion In regards to the Creator

OpinionIs GPT-4 the following big step in AI we were all waiting for?While I used to be scrolling this countless stream of tweets, the words of Sam Altman, CEO of OpenAI, were floating in...

Aligning language models to follow instructions

We’ve trained language models which can be a lot better at following user intentions than GPT-3 while also making them more truthful and fewer toxic, using techniques developed through our alignment research. These InstructGPT models, that...

Lessons learned on language model safety and misuse

We describe our latest pondering within the hope of helping other AI developers address safety and misuse of deployed models.

A hazard evaluation framework for code synthesis large language models

Codex, a big language model (LLM) trained on a wide range of codebases, exceeds the previous state-of-the-art in its capability to synthesize and generate code. Although Codex provides a plethora of advantages, models which...

Efficient training of language models to fill in the center

We show that autoregressive language models can learn to infill text after we apply a simple transformation to the dataset, which simply moves a span of text from the center of a document to...

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