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Ensembling Small Models in LLMs
In the context of Large Language Models (LLMs), ensemble learning can be straightforwardly applied to build a strong overall model by combining multiple weak models. This involves aggregating the probability distributions predicted by multiple small models or specialized submodels to derive a final prediction. Common techniques used for this aggregation step include majority voting, weighted averaging, or stacking.
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Foundations of Large Language Models
Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Ensembling Small Models in LLMs