DiscoverDeep PapersMerge, Ensemble, and Cooperate! A Survey on Collaborative LLM Strategies
Merge, Ensemble, and Cooperate! A Survey on Collaborative LLM Strategies

Merge, Ensemble, and Cooperate! A Survey on Collaborative LLM Strategies

Update: 2024-12-10
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LLMs have revolutionized natural language processing, showcasing remarkable versatility and capabilities. But individual LLMs often exhibit distinct strengths and weaknesses, influenced by differences in their training corpora. This diversity poses a challenge: how can we maximize the efficiency and utility of LLMs?

A new paper, "Merge, Ensemble, and Cooperate: A Survey on Collaborative Strategies in the Era of Large Language Models," highlights collaborative strategies to address this challenge. In this week's episode, we summarize key insights from this paper and discuss practical implications of LLM collaboration strategies across three main approaches: merging, ensemble, and cooperation. We also review some new open source models we're excited about. 


Learn more about AI observability and evaluation in our course, join the Arize AI Slack community or get the latest on LinkedIn and X.

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Merge, Ensemble, and Cooperate! A Survey on Collaborative LLM Strategies

Merge, Ensemble, and Cooperate! A Survey on Collaborative LLM Strategies

Arize AI