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Merge, Ensemble, and Cooperate! A Survey on Collaborative Strategies in the Era of Large Language Models
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The remarkable success of Large Language Models (LLMs) has ushered natural language processing (NLP) research into a new era. Despite their diverse capabilities, LLMs trained on different corpora exhibit varying strengths and weaknesses, leading to challenges in maximizing their overall efficiency and versatility. To address these challenges, recent studies have explored collaborative strategies for LLMs. This paper provides a comprehensive overview of this emerging research area, highlighting the motivation behind such collaborations. Specifically, we categorize collaborative strategies into three primary approaches: Merging, Ensemble, and Cooperation. Merging involves integrating multiple LLMs in the parameter space. Ensemble combines the outputs of various LLMs. Cooperation} leverages different LLMs to allow full play to their diverse capabilities for specific tasks. We provide in-depth introductions to these methods from different perspectives and discuss their potential applications. Additionally, we outline future research directions, hoping this work will catalyze further studies on LLM collaborations and paving the way for advanced NLP applications.
Forward citations
Cited by 7 Pith papers
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Quality-of-Service Aware LLM Routing for Edge Computing with Multiple Experts
A DRL router using graph attention state abstraction and QoS-aware rewards improves average QoS by up to 35.78% over four baselines in simulated edge LLM routing.
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Rethinking Heterogeneous LLM Merging: A Weighted Model Averaging Perspective
After truncating or expanding checkpoints to a shared shape, small-ratio weight averaging slightly improves average benchmark scores over strong Qwen sources, but headline gains are inflated by per-task best-ratio selection.
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PSO-Merging: Merging Models Based on Particle Swarm Optimization
PSO-Merging applies particle swarm optimization over model weight space, seeded with original and sparsified experts, to build multitask models that outperform existing merging baselines on several language benchmarks.
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Toward Edge General Intelligence with Multiple-Large Language Model (Multi-LLM): Architecture, Trust, and Orchestration
A survey of multi-LLM systems in edge computing, covering architectures, enabling technologies, trust mechanisms, applications, and open datasets for edge general intelligence.
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Model Merging for Knowledge Editing
R-SFT plus task-vector scaling and pruning is proposed for knowledge editing, but the claimed sequential-editing advantage is not validated by the reported experiments.
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Hierarchical Debate-Based Large Language Model (LLM) for Complex Task Planning of 6G Network Management
A hierarchical debate framework, in which LLMs first decompose a 6G task and then refine each sub-task, improves keyword coverage over one-shot and regular single-level debate on the 6GPlan benchmark.
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