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Red Teaming LLMs // Ron Heichman // #252
MLOps.community
Diversity Demands Input
Creating diverse examples requires providing a model with context from previous iterations. If the model is given the same starting context without prior examples, it is unlikely to generate significantly different outcomes. By supplying previous examples, the model can recognize repetition and adjust its outputs accordingly, thus enhancing diversity. Additionally, instructing the model explicitly to create diverse examples reinforces this learning process. This iterative interaction is comparable to mathematical methods like Newton's method, underscoring that a functional relationship exists between input and the generation of varied examples. Engaging in iterative learning effectively results in more diverse outputs.
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