Ensemble Learning for Large Language Models in Text and Code Generation: A Survey

2025-03-13

AI Summary

This paper addresses three critical challenges in large language models (LLMs): output inconsistency across single-model generations, limited pattern diversity due to inherent linguistic biases, and data privacy risks plus industrial integration barriers stemming from closed-source architectures. We systematically survey and reconceptualize LLM ensemble learning methodologies, categorizing existing techniques into seven classes and identifying four high-performance paradigms—weight merging, knowledge fusion, mixture-of-experts, and reward-based ensembling. We further propose a cross-modal transfer pathway to extend ensemble models to multimodal settings. Through joint analysis of modeling pipelines, training strategies, and output characteristics, empirical evaluation demonstrates that ensemble methods significantly improve generation diversity, quality, and task-adaptive flexibility. Our work provides both theoretical foundations and practical guidelines for industrial-scale LLM selection, customization, and deployment.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Natural Language Processing: (Large) Language Models
• Computer Vision: Large Vision Models

Application Category

• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Large language models for search

Abstract

Generative pretrained transformers (GPT) are the common large language models (LLMs) used for generating text from natural language inputs. However, the fixed properties of language parameters in individual LLMs can lead to inconsistencies in the generated outputs. This limitation also restricts the models' ability to represent diverse language patterns due to inherent biases. Moreover, many powerful LLMs are closed-source. This prevents organizations from integrating their data into these systems, raising concerns about data privacy and limiting industry applications. Inspired by the successful application of LLM ensemble models in text generation, recent literature has also investigated their potential in code generation. This article reviews these emerging LLM ensemble approaches. Our goal is to enhance readers' understanding of existing techniques and encourage further research and practical implementation, aiming to expand the real-world applications of LLM ensemble models in both text and code generation. We categorize these approaches into seven main methods: weight merging, knowledge fusion, mixture of experts, reward ensemble, output ensemble, routing, and cascading. From this list, we focus on four methods and models that show strong performance and potential for broader applications. We analyze their modeling steps, training methods, and output features to provide a clear understanding of their capabilities. Our findings highlight the benefits of LLM ensemble techniques. These include better representation of diversity, improved output quality, and greater flexibility in applications. This information offers valuable insights for selecting models for various real-world tasks involving text and code generation, and potentially applying methods to multimodal LLMs.

Problem

• Address inconsistencies in text and code generation by LLMs.
• Overcome biases and limited diversity in LLM outputs.
• Explore ensemble methods to enhance LLM flexibility and quality.

Innovation

• Ensemble learning enhances LLM diversity and output quality.
• Seven methods categorized for text and code generation.
• Focus on four high-performing ensemble techniques.