Summary:
- This paper introduces "LLM-Blender," an ensemble framework designed to improve the performance of Large Language Models (LLMs) by fusing outputs from multiple models.
- It proposes a two-stage pipeline consisting of "PairRanker" (to rank candidate outputs) and "GenFuser" (to generate a final, superior response), effectively mitigating the weaknesses of individual models.