An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models

• URL: http://arxiv.org/abs/2406.05130v1
• Date: Fri, 7 Jun 2024 17:58:11 GMT
• Title: An Empirical Study on Parameter-Efficient Fine-Tuning for MultiModal Large Language Models
• Authors: Xiongtao Zhou, Jie He, Yuhua Ke, Guangyao Zhu, Víctor Gutiérrez-Basulto, Jeff Z. Pan,
• Abstract summary: Multimodal large language models (MLLMs) have demonstrated remarkable capabilities in multimodal tasks. However, fine-tuning all parameters of MLLMs has become challenging as they usually contain billions of parameters. This paper conducts empirical studies using four popular PEFT methods to fine-tune the LLM component of open-source MLLMs.
• Score: 14.202759186103497
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal large language models (MLLMs) fine-tuned with multimodal instruction datasets have demonstrated remarkable capabilities in multimodal tasks. However, fine-tuning all parameters of MLLMs has become challenging as they usually contain billions of parameters. To address this issue, we study parameter-efficient fine-tuning (PEFT) methods for MLLMs. We aim to identify effective methods for enhancing the performance of MLLMs in scenarios where only a limited number of parameters are trained. This paper conducts empirical studies using four popular PEFT methods to fine-tune the LLM component of open-source MLLMs. We present a comprehensive analysis that encompasses various aspects, including the impact of PEFT methods on various models, parameters and location of the PEFT module, size of fine-tuning data, model stability based on PEFT methods, MLLM's generalization, and hallucination. We evaluated four PEFT methods on seven datasets from two different categories: unseen and seen datasets. Across all experiments, we show that the adapter is the best-performing PEFT method. At the same time, fine-tuning the connector layers leads to improved performance in most MLLMs. Code and data are available at https://github.com/alenai97/PEFT-MLLM.git.

Related papers

• Parameter-Efficient Fine-Tuning of Large Language Models for Unit Test Generation: An Empirical Study [3.5189934649278922]
  Large language models (LLMs) like GitHub Copilot struggle with real-world tasks without fine-tuning. This paper investigates full fine-tuning and various PEFT methods, including LoRA, (IA)3, and prompt tuning. Our findings show that PEFT methods can deliver performance comparable to full fine-tuning for unit test generation.
  arXiv  Detail & Related papers  (2024-11-04T09:03:18Z)
• LLaVA-KD: A Framework of Distilling Multimodal Large Language Models [70.19607283302712]
  We propose a novel framework to transfer knowledge from l-MLLM to s-MLLM. Specifically, we introduce Multimodal Distillation (MDist) to minimize the divergence between the visual-textual output distributions of l-MLLM and s-MLLM. We also propose a three-stage training scheme to fully exploit the potential of s-MLLM.
  arXiv  Detail & Related papers  (2024-10-21T17:41:28Z)
• Empirical Insights on Fine-Tuning Large Language Models for Question-Answering [50.12622877002846]
  Large language models (LLMs) encode extensive world knowledge through pre-training on massive datasets, which can be fine-tuned for the question-answering (QA) task. We categorize supervised fine-tuning (SFT) data based on the extent of knowledge memorized by the pretrained LLMs. Our experiments show that as few as 60 data points during the SFT stage can activate the knowledge encoded during pre-training, enabling LLMs to perform the QA task.
  arXiv  Detail & Related papers  (2024-09-24T07:38:38Z)
• The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspective [53.48484062444108]
  We find that the development of models and data is not two separate paths but rather interconnected. On the one hand, vaster and higher-quality data contribute to better performance of MLLMs; on the other hand, MLLMs can facilitate the development of data. To promote the data-model co-development for MLLM community, we systematically review existing works related to MLLMs from the data-model co-development perspective.
  arXiv  Detail & Related papers  (2024-07-11T15:08:11Z)
• Parameter-Efficient Fine-Tuning Methods for Pretrained Language Models: A Critical Review and Assessment [12.674032145667763]
  We present a comprehensive and systematic review of Efficient Fine-Tuning (PEFT) methods for pretrained language models (PLMs) PEFT offers an effective solution by reducing the number of fine-tuning parameters and memory usage while achieving comparable performance to full fine-tuning. We conduct experiments using several representative PEFT methods to better understand their effectiveness in parameter efficiency and memory efficiency.
  arXiv  Detail & Related papers  (2023-12-19T13:31:24Z)
• How to Bridge the Gap between Modalities: A Comprehensive Survey on Multimodal Large Language Model [12.890344377484759]
  This review paper explores Multimodal Large Language Models (MLLMs) MLLMs integrate Large Language Models (LLMs) like GPT-4 to handle multimodal data such as text and vision. Choosing the appropriate modality alignment method is crucial, as improper methods might require more parameters with limited performance improvement.
  arXiv  Detail & Related papers  (2023-11-10T09:51:24Z)
• When MOE Meets LLMs: Parameter Efficient Fine-tuning for Multi-task Medical Applications [57.342772288710044]
  We propose a novel parameter efficient fine-tuning framework for multi-task medical applications, dubbed as MOELoRA. For unifying MOE and LoRA, we devise multiple experts as the trainable parameters, where each expert consists of a pair of low-rank matrices to retain the small size of trainable parameters. We conduct experiments on a multi-task medical dataset, indicating MOELoRA outperforms the existing parameter efficient fine-tuning methods.
  arXiv  Detail & Related papers  (2023-10-21T17:18:09Z)
• FederatedScope-LLM: A Comprehensive Package for Fine-tuning Large Language Models in Federated Learning [70.38817963253034]
  This paper first discusses these challenges of federated fine-tuning LLMs, and introduces our package FS-LLM as a main contribution. We provide comprehensive federated parameter-efficient fine-tuning algorithm implementations and versatile programming interfaces for future extension in FL scenarios. We conduct extensive experiments to validate the effectiveness of FS-LLM and benchmark advanced LLMs with state-of-the-art parameter-efficient fine-tuning algorithms in FL settings.
  arXiv  Detail & Related papers  (2023-09-01T09:40:36Z)
• Exploring Parameter-Efficient Fine-Tuning Techniques for Code Generation with Large Language Models [12.708117108874083]
  Large Language Models (LLMs) generate code snippets given natural language intents in zero-shot, i.e., without the need for specific fine-tuning. Previous research explored In-Context Learning (ICL) as a strategy to guide the LLM generative process with task-specific prompt examples. In this paper, we deliver a comprehensive study of. PEFT techniques for LLMs under the automated code generation scenario.
  arXiv  Detail & Related papers  (2023-08-21T04:31:06Z)
• LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models [75.25782573728677]
  This paper presents a framework for adapter-based parameter-efficient fine-tuning (PEFT) of language models (LLMs) The framework includes state-of-the-art open-access LLMs such as LLaMA, BLOOM, and GPT-J, as well as widely used adapters such as Series adapters, Parallel adapter, Prompt-based learning and Reparametrization-based methods. We evaluate the effectiveness of the adapters on fourteen datasets from two different reasoning tasks, Arithmetic Reasoning and Commonsense Reasoning.
  arXiv  Detail & Related papers  (2023-04-04T16:31:37Z)

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.