It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization

• URL: http://arxiv.org/abs/2407.00487v1
• Date: Sat, 29 Jun 2024 16:34:23 GMT
• Title: It's Morphing Time: Unleashing the Potential of Multiple LLMs via Multi-objective Optimization
• Authors: Bingdong Li, Zixiang Di, Yanting Yang, Hong Qian, Peng Yang, Hao Hao, Ke Tang, Aimin Zhou,
• Abstract summary: The goal of model merging is to combine multiple models, each excelling in different tasks, into a single model that outperforms any of the individual source models. Existing methods rely heavily on human intuition and customized strategies. We propose the MM-MO method, which automates the search for optimal merging configurations using multi-objective optimization algorithms.
• Score: 16.54335356612006
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce a novel approach for large language model merging via black-box multi-objective optimization algorithms. The goal of model merging is to combine multiple models, each excelling in different tasks, into a single model that outperforms any of the individual source models. However, model merging faces two significant challenges: First, existing methods rely heavily on human intuition and customized strategies. Second, parameter conflicts often arise during merging, and while methods like DARE [1] can alleviate this issue, they tend to stochastically drop parameters, risking the loss of important delta parameters. To address these challenges, we propose the MM-MO method, which automates the search for optimal merging configurations using multi-objective optimization algorithms, eliminating the need for human intuition. During the configuration searching process, we use estimated performance across multiple diverse tasks as optimization objectives in order to alleviate the parameter conflicting between different source models without losing crucial delta parameters. We conducted comparative experiments with other mainstream model merging methods, demonstrating that our method consistently outperforms them. Moreover, our experiments reveal that even task types not explicitly targeted as optimization objectives show performance improvements, indicating that our method enhances the overall potential of the model rather than merely overfitting to specific task types. This approach provides a significant advancement in model merging techniques, offering a robust and plug-and-play solution for integrating diverse models into a unified, high-performing model.

Related papers

• Unifying Multimodal Large Language Model Capabilities and Modalities via Model Merging [103.98582374569789]
  Model merging aims to combine multiple expert models into a single model, thereby reducing storage and serving costs.<n>Previous studies have primarily focused on merging visual classification models or Large Language Models (LLMs) for code and math tasks.<n>We introduce the model merging benchmark for MLLMs, which includes multiple tasks such as VQA, Geometry, Chart, OCR, and Grounding, providing both LoRA and full fine-tuning models.
  arXiv  Detail & Related papers  (2025-05-26T12:23:14Z)
• AdaMMS: Model Merging for Heterogeneous Multimodal Large Language Models with Unsupervised Coefficient Optimization [86.8133939108057]
  We propose AdaMMS, a novel model merging method tailored for heterogeneous MLLMs. Our method tackles the challenges in three steps: mapping, merging and searching. As the first model merging method capable of merging heterogeneous MLLMs without labeled data, AdaMMS outperforms previous model merging methods on various vision-language benchmarks.
  arXiv  Detail & Related papers  (2025-03-31T05:13:02Z)
• Collab: Controlled Decoding using Mixture of Agents for LLM Alignment [90.6117569025754]
  Reinforcement learning from human feedback has emerged as an effective technique to align Large Language models. Controlled Decoding provides a mechanism for aligning a model at inference time without retraining. We propose a mixture of agent-based decoding strategies leveraging the existing off-the-shelf aligned LLM policies.
  arXiv  Detail & Related papers  (2025-03-27T17:34:25Z)
• Preference-Guided Diffusion for Multi-Objective Offline Optimization [64.08326521234228]
  We propose a preference-guided diffusion model for offline multi-objective optimization. Our guidance is a preference model trained to predict the probability that one design dominates another. Our results highlight the effectiveness of classifier-guided diffusion models in generating diverse and high-quality solutions.
  arXiv  Detail & Related papers  (2025-03-21T16:49:38Z)
• Fine, I'll Merge It Myself: A Multi-Fidelity Framework for Automated Model Merging [30.38047100067552]
  Reasoning capabilities represent a critical frontier for large language models. One way to efficiently supplement capabilities with is by model merging. We propose an Automated Model Merging Framework that enables fine-grained exploration of merging strategies.
  arXiv  Detail & Related papers  (2025-02-06T12:47:25Z)
• Modeling Multi-Task Model Merging as Adaptive Projective Gradient Descent [74.02034188307857]
  Merging multiple expert models offers a promising approach for performing multi-task learning without accessing their original data. We find existing methods inevitably discard task-specific information that, while causing conflicts, is crucial for performance. Our approach consistently outperforms previous methods, achieving state-of-the-art results across diverse architectures and tasks in both vision and NLP domains.
  arXiv  Detail & Related papers  (2025-01-02T12:45:21Z)
• Surrogate-assisted multi-objective design of complex multibody systems [1.1650821883155187]
  We present a back-and-forth approach between surrogate modeling and multi-objective optimization. We compare different strategies regarding multi-objective optimization, sampling and also surrogate modeling.
  arXiv  Detail & Related papers  (2024-12-19T13:48:49Z)
• Enhancing the Reasoning Ability of Multimodal Large Language Models via Mixed Preference Optimization [65.64108848398696]
  We introduce a preference optimization process to enhance the multimodal reasoning capabilities of MLLMs. We develop a simple yet effective method, termed Mixed Preference Optimization (MPO), which boosts multimodal CoT performance. Our model, InternVL2-8B-MPO, achieves an accuracy of 67.0 on MathVista, outperforming InternVL2-8B by 8.7 points and achieving performance comparable to the 10x larger InternVL2-76B.
  arXiv  Detail & Related papers  (2024-11-15T18:59:27Z)
• Deep Insights into Automated Optimization with Large Language Models and Evolutionary Algorithms [3.833708891059351]
  Large Language Models (LLMs) and Evolutionary Algorithms (EAs) offer promising new approach to overcome limitations and make optimization more automated. LLMs act as dynamic agents that can generate, refine, and interpret optimization strategies. EAs efficiently explore complex solution spaces through evolutionary operators.
  arXiv  Detail & Related papers  (2024-10-28T09:04:49Z)
• LLaMA-Berry: Pairwise Optimization for O1-like Olympiad-Level Mathematical Reasoning [56.273799410256075]
  The framework combines Monte Carlo Tree Search (MCTS) with iterative Self-Refine to optimize the reasoning path. The framework has been tested on general and advanced benchmarks, showing superior performance in terms of search efficiency and problem-solving capability.
  arXiv  Detail & Related papers  (2024-10-03T18:12:29Z)
• HM3: Hierarchical Multi-Objective Model Merging for Pretrained Models [28.993221775758702]
  Model merging is a technique that combines multiple large pretrained models into a single model with enhanced performance and broader task adaptability. This paper marks a significant advance toward more flexible and comprehensive model merging techniques. We train policy and value networks using offline sampling of weight vectors, which are then employed for the online optimization of merging strategies.
  arXiv  Detail & Related papers  (2024-09-27T16:31:31Z)
• Decoding-Time Language Model Alignment with Multiple Objectives [116.42095026960598]
  Existing methods primarily focus on optimizing LMs for a single reward function, limiting their adaptability to varied objectives. Here, we propose $textbfmulti-objective decoding (MOD)$, a decoding-time algorithm that outputs the next token from a linear combination of predictions. We show why existing approaches can be sub-optimal even in natural settings and obtain optimality guarantees for our method.
  arXiv  Detail & Related papers  (2024-06-27T02:46:30Z)
• Towards Efficient Pareto Set Approximation via Mixture of Experts Based Model Fusion [53.33473557562837]
  Solving multi-objective optimization problems for large deep neural networks is a challenging task due to the complexity of the loss landscape and the expensive computational cost. We propose a practical and scalable approach to solve this problem via mixture of experts (MoE) based model fusion. By ensembling the weights of specialized single-task models, the MoE module can effectively capture the trade-offs between multiple objectives.
  arXiv  Detail & Related papers  (2024-06-14T07:16:18Z)
• Unleashing the Potential of Large Language Models as Prompt Optimizers: An Analogical Analysis with Gradient-based Model Optimizers [108.72225067368592]
  We propose a novel perspective to investigate the design of large language models (LLMs)-based prompts. We identify two pivotal factors in model parameter learning: update direction and update method. In particular, we borrow the theoretical framework and learning methods from gradient-based optimization to design improved strategies.
  arXiv  Detail & Related papers  (2024-02-27T15:05:32Z)
• Pareto Set Learning for Neural Multi-objective Combinatorial Optimization [6.091096843566857]
  Multiobjective optimization (MOCO) problems can be found in many real-world applications. We develop a learning-based approach to approximate the whole Pareto set for a given MOCO problem without further search procedure. Our proposed method significantly outperforms some other methods on the multiobjective traveling salesman problem, multiconditioned vehicle routing problem and multi knapsack problem in terms of solution quality, speed, and model efficiency.
  arXiv  Detail & Related papers  (2022-03-29T09:26:22Z)
• Leveraging Trust for Joint Multi-Objective and Multi-Fidelity Optimization [0.0]
  This paper investigates a novel approach to Bayesian multi-objective and multi-fidelity (MOMF) optimization. We suggest the innovative use of a trust metric to support simultaneous optimization of multiple objectives and data sources. Our methods offer broad applicability in solving simulation problems in fields such as plasma physics and fluid dynamics.
  arXiv  Detail & Related papers  (2021-12-27T20:55:26Z)
• A survey on multi-objective hyperparameter optimization algorithms for Machine Learning [62.997667081978825]
  This article presents a systematic survey of the literature published between 2014 and 2020 on multi-objective HPO algorithms. We distinguish between metaheuristic-based algorithms, metamodel-based algorithms, and approaches using a mixture of both. We also discuss the quality metrics used to compare multi-objective HPO procedures and present future research directions.
  arXiv  Detail & Related papers  (2021-11-23T10:22:30Z)
• MODRL/D-AM: Multiobjective Deep Reinforcement Learning Algorithm Using Decomposition and Attention Model for Multiobjective Optimization [15.235261981563523]
  This paper proposes a multiobjective deep reinforcement learning method to solve multiobjective optimization problem. In our method, each subproblem is solved by an attention model, which can exploit the structure features as well as node features of input nodes.
  arXiv  Detail & Related papers  (2020-02-13T12:59:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.