Survey paper details ensemble learning for LLMs in text and code generation

A new survey paper published on arXiv details various ensemble learning techniques for large language models (LLMs) in both text and code generation. The paper, authored by Jingzhi Gong, categorizes these methods into seven main approaches, including weight merging, knowledge fusion, and mixture-of-experts. The research highlights the benefits of ensemble methods such as improved diversity, enhanced output quality, and increased application flexibility, aiming to guide future research and practical implementation. AI

IMPACT Provides a structured overview of ensemble techniques to improve LLM performance and flexibility in text and code generation.

RANK_REASON The cluster contains an academic survey paper on ensemble learning for LLMs. [lever_c_demoted from research: ic=1 ai=1.0]

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• arXiv
• DagsHub
• Generative Pretrained Transformers
• Hugging Face
• Jingzhi Gong
• large-language models

• paper
• model release

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