A team of AI researchers at Google's DeepMind project, working with a colleague from the University of Southern California, has developed a vehicle for allowing large language models (LLMs) to find and use task-intrinsic reasoning structures as a means for improving returned results.

The group has written a paper describing their framework and outlining how well it has tested thus far, and have posted it on the arXiv preprint server. They have also posted a copy of the paper on Hugging Face, a and data science platform.

Large language models, such as ChatGPT, are able to return human-like responses to queries by users by scouring the Internet for information and using it to create text in a human-like way by mimicking how humans write. But such models are still quite limited in their abilities due to their simple nature. In this new study, researchers at DeepMind have tweaked the model used by LLMs to improve results.

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