In-context learning works by putting important information and examples into the prompt context itself, and it can be p…
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In-context learning works by putting important information and examples into the prompt context itself, and it can be pretty effective for many use cases—but not all. In her recent conversation with Ben Lorica, AMD’s Sharon Zhou detailed the benefits and trade-offs of in-context learning and supervised fine-tuning, explaining when you may want to use one over the other.
Tags: Reel · Fine Tuning · In Context Learning · Model Latency
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