Contributed Talk
in
Workshop: Generative AI and Creativity: A dialogue between machine learning researchers and creative professionals
Steering LLMs to Evaluate and Amplify Creativity
Matthew Olson · Neale Ratzlaff · Musashi Hinck · Shao-Yen Tseng · VASUDEV LAL
Abstract:
Although capable of generating creative texts, LLMs are poor judges of what constitutes creativity''. In this paper, we show that we can leverage this knowledge of \textit{how} to write creatively in order to judge \textit{what} is creative. Our mechanistic approach extracts differences in the internal states of the LLM when being prompted to write
boringly'' or ``creatively'' to provide a more robust measure of creativity that corresponds more strongly with human judgment. We also show these internal state differences can be applied to enhance the creativity of generated texts.
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