Teaching GPT-3 to express its uncertainty
It’s fascinating how in a few years, large language models (LLM) went from intriguing new deep learning models (the transformer architecture) to one of the hottest areas of AI research. Of special interest is the capacity of LLMs like OpenAI’s GPT-3 and DeepMind’s Gopher to generate long sequences of (mostly) coherent text.
But one of the problems with LLMs is that they always have an answer to your prompt, even if that answer is completely wrong. And there have been numerous cases of LLMs making wrong claims and generating text that, although impressive, is utter nonsense.
LLMs are gradually finding their way into real-world applications, from composing emails and writing articles, to answering questions and filling in for customer service agents. Accordingly, there is growing interest in finding ways to determine the reliability and trustfulness of the answers these machine learning models produce. According to a new study by researchers at OpenAI and the University of Oxford, large language models can be calibrated to express their level of certainty in the answers they provide. The study, which focuses on GPT-3, shows that with the right training, LLMs can contribute to making AI systems aligned with human goals and intents.
Read the full article on TechTalks.
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