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TOMGPT: Reliable Text-Only Training Approach for Cost-Effective Multi-modal Large Language Model

Published: 19 June 2024 Publication History

Abstract

Multi-modal large language models (MLLMs), such as GPT-4, exhibit great comprehension capabilities on human instruction, as well as zero-shot ability on new downstream multi-modal tasks. To integrate the different modalities within a unified embedding space, previous MLLMs attempted to conduct visual instruction tuning with massive and high-quality image-text pair data, which requires substantial costs in data collection and training resources. In this article, we propose TOMGPT (Text-Only training Multi-modal GPT), a cost-effective MLLM tuned solely on easily accessible text data with much fewer resources. Along with pre-trained visual-linguistic coupled modality space (e.g., CLIP and ALIGN model), a text-only training strategy is devised to further project the aligned multi-modal latent space to that of LLM, endowing the LLM with visual comprehension capabilities in an efficient manner. Instead of enormous image-text training data required by previous MLLMs, we find that TOMGPT can be well-tuned with fewer yet diverse GPT-generated free-form text data, as we establish the semantic connection between LLM and pre-trained vision-language model. A quantitative evaluation is conducted on both MME and LVLM, which are recently released and extensively utilized MLLM benchmarks. The experiments reveal that TOMGPT achieved reliable performance compared to numerous models trained on a large amount of image-text pair data. Case studies are also presented, demonstrating TOMGPT’s broad understanding and dialogue capabilities across diverse image categories.

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cover image ACM Transactions on Knowledge Discovery from Data
ACM Transactions on Knowledge Discovery from Data  Volume 18, Issue 7
August 2024
505 pages
EISSN:1556-472X
DOI:10.1145/3613689
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 19 June 2024
Online AM: 28 March 2024
Accepted: 17 March 2024
Revised: 10 February 2024
Received: 15 September 2023
Published in TKDD Volume 18, Issue 7

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  1. Multi-modal
  2. large language model
  3. text-only training

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  • (2024)State of the Art and Potentialities of Graph-level LearningACM Computing Surveys10.1145/3695863Online publication date: 12-Sep-2024
  • (2024)Exploiting Instance-level Relationships in Weakly Supervised Text-to-Video RetrievalACM Transactions on Multimedia Computing, Communications, and Applications10.1145/366357120:10(1-21)Online publication date: 12-Sep-2024
  • (2024)Adversarial attacks and defenses for large language models (LLMs): methods, frameworks & challengesInternational Journal of Multimedia Information Retrieval10.1007/s13735-024-00334-813:3Online publication date: 25-Jun-2024
  • (2024)Large language models for generative information extraction: a surveyFrontiers of Computer Science10.1007/s11704-024-40555-y18:6Online publication date: 11-Nov-2024

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