MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples

• URL: http://arxiv.org/abs/2312.06363v3
• Date: Mon, 12 Aug 2024 06:17:21 GMT
• Title: MMICT: Boosting Multi-Modal Fine-Tuning with In-Context Examples
• Authors: Tao Chen, Enwei Zhang, Yuting Gao, Ke Li, Xing Sun, Yan Zhang, Hui Li, Rongrong Ji,
• Abstract summary: This paper introduces Multi-Modal In-Context Tuning (MMICT), a novel multi-modal fine-tuning paradigm. We propose the Multi-Modal Hub (M-Hub), a unified module that captures various multi-modal features according to different inputs and objectives. Based on M-Hub, MMICT enables MM-LLMs to learn from in-context visual-guided textual features and subsequently generate outputs conditioned on the textual-guided visual features.
• Score: 63.78384552789171
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although In-Context Learning (ICL) brings remarkable performance gains to Large Language Models (LLMs), the improvements remain lower than fine-tuning on downstream tasks. This paper introduces Multi-Modal In-Context Tuning (MMICT), a novel multi-modal fine-tuning paradigm that boosts multi-modal fine-tuning by fully leveraging the promising ICL capability of multi-modal LLMs (MM-LLMs). We propose the Multi-Modal Hub (M-Hub), a unified module that captures various multi-modal features according to different inputs and objectives. Based on M-Hub, MMICT enables MM-LLMs to learn from in-context visual-guided textual features and subsequently generate outputs conditioned on the textual-guided visual features. Moreover, leveraging the flexibility of M-Hub, we design a variety of in-context demonstrations. Extensive experiments on a diverse range of downstream multi-modal tasks demonstrate that MMICT significantly outperforms traditional fine-tuning strategy and the vanilla ICT method that directly takes the concatenation of all information from different modalities as input. Our implementation is available at: https://github.com/KDEGroup/MMICT.

Related papers

• LLMs Can Evolve Continually on Modality for X-Modal Reasoning [62.2874638875554]
  Existing methods rely heavily on modal-specific pretraining and joint-modal tuning, leading to significant computational burdens when expanding to new modalities. We propose PathWeave, a flexible and scalable framework with modal-Path sWitching and ExpAnsion abilities. PathWeave performs comparably to state-of-the-art MLLMs while concurrently reducing parameter training burdens by 98.73%.
  arXiv  Detail & Related papers  (2024-10-26T13:19:57Z)
• M$^2$PT: Multimodal Prompt Tuning for Zero-shot Instruction Learning [90.75075886543404]
  Multimodal Large Language Models (MLLMs) demonstrate remarkable performance across a wide range of domains. In this work, we introduce a novel Multimodal Prompt Tuning (M$2$PT) approach for efficient instruction tuning of MLLMs.
  arXiv  Detail & Related papers  (2024-09-24T01:40:24Z)
• What Makes Multimodal In-Context Learning Work? [58.48612721156335]
  We present a framework for investigating Multimodal ICL (M-ICL) in the context of Large Multimodal Models. M-ICL primarily relies on text-driven mechanisms, showing little to no influence from the image modality. We identify several biases and limitations of M-ICL that warrant consideration prior to deployment.
  arXiv  Detail & Related papers  (2024-04-24T08:50:45Z)
• Tuning Large Multimodal Models for Videos using Reinforcement Learning from AI Feedback [38.708690624594794]
  Video and text multimodal alignment remains challenging, primarily due to the deficient volume and quality of multimodal instruction-tune data. We present a novel alignment strategy that employs multimodal AI system to oversee itself called Reinforcement Learning from AI Feedback (RLAIF) In specific, we propose context-aware reward modeling by providing detailed video descriptions as context during the generation of preference feedback.
  arXiv  Detail & Related papers  (2024-02-06T06:27:40Z)
• Lightweight In-Context Tuning for Multimodal Unified Models [57.10831399642176]
  MultiModal In-conteXt Tuning (M$2$IXT) is a lightweight module to enhance the ICL capabilities of multimodal unified models. When tuned on as little as 50K multimodal data, M$2$IXT can boost the few-shot ICL performance significantly.
  arXiv  Detail & Related papers  (2023-10-08T10:47:24Z)
• AnyMAL: An Efficient and Scalable Any-Modality Augmented Language Model [33.072967177313025]
  We present Any-Modality Augmented Language Model (AnyMAL), a unified model that reasons over diverse input modality signals. AnyMAL inherits the powerful text-based reasoning abilities of the state-of-the-art LLMs including LLaMA-2 (70B) We conduct comprehensive empirical analysis comprising both human and automatic evaluations, and demonstrate state-of-the-art performance on various multimodal tasks.
  arXiv  Detail & Related papers  (2023-09-27T22:50:51Z)
• Macaw-LLM: Multi-Modal Language Modeling with Image, Audio, Video, and Text Integration [50.94902442781148]
  We propose a novel multi-modal large language model (LLM) that seamlessly integrates visual, audio, and textual information. Macaw-LLM consists of three main components: a modality module for encoding multi-modal data, a cognitive module for harnessing pretrained LLMs, and an alignment module for harmonizing diverse representations. We construct a large-scale multi-modal instruction dataset in terms of multi-turn dialogue, including 69K image instances and 50K video instances.
  arXiv  Detail & Related papers  (2023-06-15T12:45:25Z)

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.