Combining Multiple Models (Ensemble) in LLM
Contents
Combining Multiple Models (Ensemble) in LLMยถ
Summaryยถ
Combining multiple models, also known as ensemble learning, is a technique used in Large Language Models (LLMs) to improve performance by leveraging the strengths of individual models. This approach involves training multiple models and then combining their outputs to produce a more accurate and robust final prediction. Ensemble methods can help mitigate the weaknesses of single-model approaches, reduce overfitting, and enhance generalization capacity. In the context of LLMs, ensemble learning can harness the diverse capabilities of individual models to achieve superior results.
Key Conceptsยถ
Ensemble Learning : A technique that combines the outputs of multiple models to produce a more accurate and robust final prediction.
Bagging (Bootstrap Aggregating) : A method that involves creating multiple subsets of the original dataset using bootstrap sampling and training a separate model on each subset.
Boosting : A sequential ensemble method where models are trained one after another, each new model focusing on the errors made by the previous models.
Stacking : A method that involves training multiple base models and then using their predictions as inputs to a higher-level meta-model.
LLM-Blender : A simple ensemble learning framework that ranks and merges outputs from various LLMs using pairwise comparison and generative fusion.
Referencesยถ
URL Name |
URL |
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arXiv: Merge, Ensemble, and Cooperate |
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Data Science Dojo: Ensemble Methods in Machine Learning |
https://datasciencedojo.com/blog/ensemble-methods-in-machine-learning/ |
NCBI: One LLM is not Enough |
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Allen AI: LLM-Blender |
https://blog.allenai.org/llm-blender-a-simple-ensemble-learning-framework-for-llms-9e4bc57af23e |
Dida: Ensembles in Machine Learning |