Language Model Ensembles
Overview
Language model ensembles study how to combine multiple models so their complementary strengths can improve answers, predictions, or evaluations. Common approaches include majority and weighted voting, learned stacking and calibration, query-level routing, logit fusion, and deliberation or verification across agents. These methods are tested on tasks such as mathematical reasoning, question answering, forecasting, text classification, and evaluation by LLM judges, with attention to accuracy, uncertainty, and inference cost. A central tension is that ensembles help most when models make different errors, yet correlated failures are common and adding models or computation does not reliably outperform the strongest individual model.
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