Surveying the MLLM Landscape: A Meta-Review of Current Surveys

• URL: http://arxiv.org/abs/2409.18991v1
• Date: Tue, 17 Sep 2024 14:35:38 GMT
• Title: Surveying the MLLM Landscape: A Meta-Review of Current Surveys
• Authors: Ming Li, Keyu Chen, Ziqian Bi, Ming Liu, Benji Peng, Qian Niu, Junyu Liu, Jinlang Wang, Sen Zhang, Xuanhe Pan, Jiawei Xu, Pohsun Feng,
• Abstract summary: Multimodal Large Language Models (MLLMs) have become a transformative force in the field of artificial intelligence. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs.
• Score: 17.372501468675303
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rise of Multimodal Large Language Models (MLLMs) has become a transformative force in the field of artificial intelligence, enabling machines to process and generate content across multiple modalities, such as text, images, audio, and video. These models represent a significant advancement over traditional unimodal systems, opening new frontiers in diverse applications ranging from autonomous agents to medical diagnostics. By integrating multiple modalities, MLLMs achieve a more holistic understanding of information, closely mimicking human perception. As the capabilities of MLLMs expand, the need for comprehensive and accurate performance evaluation has become increasingly critical. This survey aims to provide a systematic review of benchmark tests and evaluation methods for MLLMs, covering key topics such as foundational concepts, applications, evaluation methodologies, ethical concerns, security, efficiency, and domain-specific applications. Through the classification and analysis of existing literature, we summarize the main contributions and methodologies of various surveys, conduct a detailed comparative analysis, and examine their impact within the academic community. Additionally, we identify emerging trends and underexplored areas in MLLM research, proposing potential directions for future studies. This survey is intended to offer researchers and practitioners a comprehensive understanding of the current state of MLLM evaluation, thereby facilitating further progress in this rapidly evolving field.

Related papers

• MME-Survey: A Comprehensive Survey on Evaluation of Multimodal LLMs [97.94579295913606]
  Multimodal Large Language Models (MLLMs) have garnered increased attention from both industry and academia. In the development process, evaluation is critical since it provides intuitive feedback and guidance on improving models. This work aims to offer researchers an easy grasp of how to effectively evaluate MLLMs according to different needs and to inspire better evaluation methods.
  arXiv  Detail & Related papers  (2024-11-22T18:59:54Z)
• Large Language Model for Qualitative Research -- A Systematic Mapping Study [3.302912592091359]
  Large Language Models (LLMs), powered by advanced generative AI, have emerged as transformative tools. This study systematically maps the literature on the use of LLMs for qualitative research. Findings reveal that LLMs are utilized across diverse fields, demonstrating the potential to automate processes.
  arXiv  Detail & Related papers  (2024-11-18T21:28:00Z)
• Understanding the Role of LLMs in Multimodal Evaluation Benchmarks [77.59035801244278]
  This paper investigates the role of the Large Language Model (LLM) backbone in Multimodal Large Language Models (MLLMs) evaluation. Our study encompasses four diverse MLLM benchmarks and eight state-of-the-art MLLMs. Key findings reveal that some benchmarks allow high performance even without visual inputs and up to 50% of error rates can be attributed to insufficient world knowledge in the LLM backbone.
  arXiv  Detail & Related papers  (2024-10-16T07:49:13Z)
• A Survey on Multimodal Benchmarks: In the Era of Large AI Models [13.299775710527962]
  Multimodal Large Language Models (MLLMs) have brought substantial advancements in artificial intelligence. This survey systematically reviews 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application.
  arXiv  Detail & Related papers  (2024-09-21T15:22:26Z)
• A Survey on Evaluation of Multimodal Large Language Models [11.572066870077888]
  Multimodal Large Language Models (MLLMs) mimic human perception and reasoning system by integrating powerful Large Language Models (LLMs) This framework endows MLLMs with human-like capabilities, and suggests a potential pathway towards achieving artificial general intelligence (AGI) With the emergence of all-round MLLMs like GPT-4V and Gemini, a multitude of evaluation methods have been developed to assess their capabilities across different dimensions.
  arXiv  Detail & Related papers  (2024-08-28T13:05:55Z)
• A Comprehensive Review of Multimodal Large Language Models: Performance and Challenges Across Different Tasks [74.52259252807191]
  Multimodal Large Language Models (MLLMs) address the complexities of real-world applications far beyond the capabilities of single-modality systems. This paper systematically sorts out the applications of MLLM in multimodal tasks such as natural language, vision, and audio.
  arXiv  Detail & Related papers  (2024-08-02T15:14:53Z)
• A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challenges [60.546677053091685]
  Large language models (LLMs) have unlocked novel opportunities for machine learning applications in the financial domain. We explore the application of LLMs on various financial tasks, focusing on their potential to transform traditional practices and drive innovation. We highlight this survey for categorizing the existing literature into key application areas, including linguistic tasks, sentiment analysis, financial time series, financial reasoning, agent-based modeling, and other applications.
  arXiv  Detail & Related papers  (2024-06-15T16:11:35Z)
• Exploring the Reasoning Abilities of Multimodal Large Language Models (MLLMs): A Comprehensive Survey on Emerging Trends in Multimodal Reasoning [44.12214030785711]
  We review the existing evaluation protocols of multimodal reasoning, categorize and illustrate the frontiers of Multimodal Large Language Models (MLLMs) We introduce recent trends in applications of MLLMs on reasoning-intensive tasks and discuss current practices and future directions.
  arXiv  Detail & Related papers  (2024-01-10T15:29:21Z)
• A Survey on Evaluation of Large Language Models [87.60417393701331]
  Large language models (LLMs) are gaining increasing popularity in both academia and industry. This paper focuses on three key dimensions: what to evaluate, where to evaluate, and how to evaluate.
  arXiv  Detail & Related papers  (2023-07-06T16:28:35Z)
• A Survey on Multimodal Large Language Models [71.63375558033364]
  Multimodal Large Language Model (MLLM) represented by GPT-4V has been a new rising research hotspot. This paper aims to trace and summarize the recent progress of MLLMs.
  arXiv  Detail & Related papers  (2023-06-23T15:21:52Z)

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.