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Machine Learning Street Talk (MLST) cover image

Open-Ended AI: The Key to Superhuman Intelligence? - Prof. Tim Rocktäschel

Machine Learning Street Talk (MLST)

NOTE

Prompting Shapes Performance

The effectiveness of large language models can be significantly influenced by the way they are prompted. For instance, simple prompts that encourage step-by-step reasoning, such as asking the model to think through a math problem systematically, can lead to improved problem-solving capabilities. This concept aligns with earlier theories of self-improvement in neural networks, where altering the internal weight settings of the model could adjust its behavior. Essentially, the change in performance stems from modifying input prompts, demonstrating a direct link between prompting strategies and model functionality.

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