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2024

Prompt-Time Symbolic Knowledge Capture with Large Language Models

Çöplü, Tolga, Bendiken, Arto, Skomorokhov, Andrii et al.

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Augmenting large language models (LLMs) with user-specific knowledge is crucial for real-world applications, such as personal AI assistants.

However, LLMs inherently lack mechanisms for prompt-driven knowledge capture.
This paper investigates utilizing the existing LLM capabilities to enable prompt-driven knowledge capture, with a particular emphasis on knowledge graphs.
We address this challenge by focusing on prompt-to-triple (P2T) generation.

Prompt-Time Symbolic Knowledge Capture with Large Language Models · Around

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Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2005
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