by jsendak | Sep 30, 2024 | AI
arXiv:2409.18142v1 Announce Type: new
Abstract: The rapid evolution of Multimodal Large Language Models (MLLMs) has brought substantial advancements in artificial intelligence, significantly enhancing the capability to understand and generate multimodal content. While prior studies have largely concentrated on model architectures and training methodologies, a thorough analysis of the benchmarks used for evaluating these models remains underexplored. This survey addresses this gap by systematically reviewing 211 benchmarks that assess MLLMs across four core domains: understanding, reasoning, generation, and application. We provide a detailed analysis of task designs, evaluation metrics, and dataset constructions, across diverse modalities. We hope that this survey will contribute to the ongoing advancement of MLLM research by offering a comprehensive overview of benchmarking practices and identifying promising directions for future work. An associated GitHub repository collecting the latest papers is available.
The Significance of Multimodal Large Language Models (MLLMs)
Over the years, Multimodal Large Language Models (MLLMs) have witnessed rapid evolution, revolutionizing the field of artificial intelligence. These models have significantly enhanced our capability to understand and generate multimodal content, which has numerous practical applications across various industries. However, while researchers have focused primarily on model architectures and training methodologies, the benchmarks used to evaluate these models have received limited attention.
This survey aims to bridge this gap by systematically reviewing 211 benchmarks that assess MLLMs across four fundamental domains: understanding, reasoning, generation, and application. By diving deep into the task designs, evaluation metrics, and dataset constructions, the survey sheds light on the intricacies of evaluating MLLMs across diverse modalities.
The Multi-Disciplinary Nature of MLLM Research
One of the key takeaways from this survey is the multi-disciplinary nature of MLLM research. Due to the complex nature of multimodal content, effectively evaluating MLLMs requires expertise from various fields. Linguists, computer scientists, psychologists, and domain experts from different industries must collaborate to construct meaningful benchmarks that capture the richness and complexity of multimodal data.
These benchmarks are not limited to a single modality; instead, they encompass a wide range of input types, including text, images, videos, and audio. The diverse nature of the benchmarks ensures that MLLMs are tested against real-world scenarios, where multimodal content is inherently entangled, requiring the models to understand and generate content in a coherent and meaningful manner.
Identifying Promising Directions for Future Work
By analyzing the current benchmarking practices, this survey also identifies several promising directions for future MLLM research. One notable area is the development of more comprehensive and challenging benchmarks that can better evaluate MLLMs’ capabilities. These benchmarks should strive to capture the nuances and context-dependent nature of multimodal content, providing opportunities for innovative research and development of MLLMs.
In addition, the survey emphasizes the importance of standardized evaluation metrics and guidelines for benchmarking MLLMs. This standardization would enable fair comparisons between different models and facilitate progress in the field. Researchers should work towards consensus on evaluation metrics, considering factors such as objectivity, interpretability, and alignment with human judgment.
The associated GitHub repository, which collects the latest papers in the field, serves as a valuable resource for researchers and practitioners seeking to stay updated on the advancements in MLLM research.
Conclusion
This survey provides a comprehensive overview of benchmarking practices for Multimodal Large Language Models (MLLMs). It highlights the multi-disciplinary nature of MLLM research, which requires collaboration between experts from various fields. The survey also identifies promising directions for future work, emphasizing the need for more challenging benchmarks and standardized evaluation metrics. By addressing these considerations, researchers can further advance the capabilities of MLLMs and unlock their potential in understanding and generating multimodal content.
Keywords: Multimodal Large Language Models, MLLMs, benchmarking practices, evaluation metrics, multimodal content.
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by jsendak | Sep 21, 2024 | Computer Science
arXiv:2409.12319v1 Announce Type: cross
Abstract: Multimodal large language models (MLLMs) have recently become a focal point of research due to their formidable multimodal understanding capabilities. For example, in the audio and speech domains, an LLM can be equipped with (automatic) speech recognition (ASR) abilities by just concatenating the audio tokens, computed with an audio encoder, and the text tokens to achieve state-of-the-art results. On the contrary, tasks like visual and audio-visual speech recognition (VSR/AVSR), which also exploit noise-invariant lip movement information, have received little or no attention. To bridge this gap, we propose Llama-AVSR, a new MLLM with strong audio-visual speech recognition capabilities. It leverages pre-trained audio and video encoders to produce modality-specific tokens which, together with the text tokens, are processed by a pre-trained LLM (e.g., Llama3.1-8B) to yield the resulting response in an auto-regressive fashion. Llama-AVSR requires a small number of trainable parameters as only modality-specific projectors and LoRA modules are trained whereas the multi-modal encoders and LLM are kept frozen. We evaluate our proposed approach on LRS3, the largest public AVSR benchmark, and we achieve new state-of-the-art results for the tasks of ASR and AVSR with a WER of 0.81% and 0.77%, respectively. To bolster our results, we investigate the key factors that underpin the effectiveness of Llama-AVSR: the choice of the pre-trained encoders and LLM, the efficient integration of LoRA modules, and the optimal performance-efficiency trade-off obtained via modality-aware compression rates.
Analysis: Multimodal Large Language Models in Multimedia Information Systems
The concept of multimodal large language models (MLLMs) is a cutting-edge area of research that combines artificial intelligence, natural language processing, and computer vision to enhance the understanding of textual, audio, and visual information. MLLMs have gained significant attention due to their impressive capabilities in analyzing multimodal data and performing various tasks such as speech recognition, image captioning, and video understanding.
In this particular study, the focus is on the audio and visual domains, specifically audio-visual speech recognition (AVSR). While ASR (automatic speech recognition) has advanced significantly with the help of MLLMs, AVSR has received relatively little attention. AVSR is a challenging task as it requires understanding not only the audio but also the visual signals, particularly lip movement.
The proposed model, Llama-AVSR, aims to bridge this gap in AVSR research. It leverages pre-trained audio and video encoders to extract modality-specific tokens, which are then combined with text tokens and processed through a pre-trained LLM. By adopting an auto-regressive approach, Llama-AVSR generates highly accurate responses for both ASR and AVSR tasks.
One key aspect of Llama-AVSR is the use of frozen multi-modal encoders and LLM, which means that they are not fine-tuned during training. Instead, only modality-specific projectors and LoRA (Local Refinement Attention) modules are trained. This approach allows for efficient training and parameter optimization while still achieving state-of-the-art results.
The authors evaluate Llama-AVSR using the LRS3 benchmark, which is a widely-used dataset for AVSR. The results demonstrate the effectiveness of the proposed approach, achieving a Word Error Rate (WER) of 0.81% for ASR and 0.77% for AVSR, which are new state-of-the-art performances.
This study showcases the interdisciplinary nature of multimedia information systems, combining elements from audio processing, computer vision, and natural language understanding. By integrating audio and visual information, MLLMs like Llama-AVSR have the potential to revolutionize various applications such as speech recognition systems, virtual reality experiences, and interactive multimedia content.
Implications for Animations, Artificial Reality, Augmented Reality, and Virtual Realities
Animations, artificial reality, augmented reality, and virtual realities greatly benefit from the advancements in multimodal large language models such as Llama-AVSR. The ability to understand and process both audio and visual information opens up new possibilities for creating immersive and realistic experiences in these domains.
In the context of animations, MLLMs can enhance the creation and synchronization of animated characters’ lip movements with the corresponding dialogue or speech. By utilizing the lip movement information, as considered in AVSR, animations can be more accurate and lifelike. This can significantly improve the quality of animated movies, TV shows, and video games, bringing characters to life in a way that closely matches the intended audio.
For artificial reality, MLLMs can play a crucial role in bridging the gap between artificial intelligence and virtual environments. By understanding and responding to multimodal inputs from users, AI-powered virtual agents or characters can engage in more realistic and natural interactions. This can enhance the overall user experience, making artificial reality environments feel more immersive and interactive.
In augmented reality applications, MLLMs like Llama-AVSR can contribute to more accurate speech recognition and understanding. For example, in AR systems that involve voice commands or speech-based interactions, having a robust AVSR capability can improve the accuracy of speech recognition, enabling more intuitive and reliable interactions between users and augmented environments.
Virtual reality experiences can also benefit from the advancements in MLLMs. By analyzing both audio and visual cues, virtual reality systems can provide more realistic and context-aware simulations. For instance, within a virtual reality game, the recognition of audio-visual speech can be used to enhance the understanding of the player’s voice commands and facilitate more intelligent and immersive gameplay.
In conclusion, the development of multimodal large language models, exemplified by Llama-AVSR, has far-reaching implications for multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By combining expertise from multiple disciplines, these models open up exciting possibilities for advanced multimodal processing and more immersive and realistic experiences in various domains.
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by jsendak | Sep 18, 2024 | Computer Science
Expert Commentary: Generating Geometric Images for Mathematical Reasoning
Large language models have shown great promise in various NLP tasks, but their capabilities in mathematical reasoning have only recently started to be explored. Previous research has mainly focused on text-based algebra problems, leaving a gap in the study of geometry. One of the main challenges in advancing research in this area has been the lack of high-quality geometric datasets.
This paper introduces AutoGeo, a novel approach for automatically generating mathematical geometric images. By leveraging precisely defined geometric clauses, AutoGeo is able to create a diverse range of geometry image-text pairs. This includes various geometric shapes such as lines, polygons, circles, and complex spatial relationships.
The creation of AutoGeo-100k, an extensive repository comprising 100k high-quality geometry image-text pairs, is a significant contribution of this work. This dataset will not only fill the critical gap in the availability of geometric datasets but also fuel further research and development of sophisticated AI-driven tools in education and research.
One of the key applications of AutoGeo-100k is enhancing the performance of multimodal large language models through fine-tuning. Experimental results have shown that these models trained on AutoGeo-100k exhibit improved accuracy in tasks like geometric captioning and mathematical reasoning. This indicates the effectiveness of AutoGeo-100k in enhancing the model’s ability to handle geometric images.
The implications of this research are far-reaching. The availability of AutoGeo-100k will not only enable the development of AI models that can understand and reason about geometric problems but also help in the development of AI-driven educational tools. Such tools can provide personalized feedback and assistance to students studying geometry, making the learning process more interactive and engaging.
Furthermore, this work opens up new possibilities for research in the intersection of AI and geometry. Researchers can now explore how large language models can be utilized to solve complex geometric problems, paving the way for more sophisticated AI algorithms in the field.
In conclusion, the introduction of AutoGeo and the creation of AutoGeo-100k dataset address the lack of high-quality geometric datasets and significantly contribute to the advancement of AI-driven tools in education and research. This research serves as a milestone in the exploration of large language models’ capabilities in mathematical reasoning and opens up exciting avenues for future research in the field.
Project page: https://this_url.
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by jsendak | Sep 15, 2024 | Computer Science
Analyzing Electron Microscopy Images in Semiconductor Manufacturing with Vision-Language Instruction Tuning
In the field of semiconductor manufacturing, the analysis and interpretation of electron microscopy images play a crucial role in quality control and process optimization. However, this task can be time-consuming and tedious, requiring extensive human labeling and domain-specific expertise. To address these challenges, a novel framework has been developed that leverages vision-language instruction tuning to analyze and interpret microscopy images.
The Teacher-Student Approach
The framework employs a unique teacher-student approach, utilizing pre-trained multimodal large language models like GPT-4 as the “teacher” to generate instruction-following data for zero-shot visual question answering (VQA) and classification tasks. The generated data is then used to customize smaller multimodal models (SMMs) for microscopy image analysis, resulting in an instruction-tuned language-and-vision assistant.
This teacher-student approach provides several advantages. Firstly, it significantly reduces the need for extensive human labeling, as the teacher model can generate large amounts of instruction-following data automatically. This not only saves time and resources but also eliminates potential human biases in the labeling process. Furthermore, the customization of smaller multimodal models allows for a more tailored analysis of microscopy images, taking into account the specific requirements and characteristics of semiconductor manufacturing.
Merging Knowledge Engineering with Machine Learning
One of the key strengths of this framework is the integration of domain-specific expertise from larger to smaller multimodal models. By combining knowledge engineering and machine learning techniques, the framework ensures that the SMMs have access to the accumulated knowledge and insights captured by the larger models. This integration enables the smaller models to benefit from the vast amount of pre-existing knowledge, enhancing their performance in microscopy image analysis.
A Secure, Cost-Effective, and Customizable Approach
Another important aspect addressed by this framework is the challenge of adopting proprietary models in semiconductor manufacturing. By leveraging the teacher-student approach, the framework allows the use of pre-trained models like GPT-4 without the need for sharing proprietary data. This not only ensures data security but also makes the approach more cost-effective, as the use of pre-trained models eliminates the need for training from scratch.
Furthermore, the framework can be easily customized to adapt to different requirements and applications within semiconductor manufacturing. The instruction-tuned language-and-vision assistant can be fine-tuned to specific tasks and datasets, allowing for a more accurate and efficient analysis of electron microscopy images.
Future Perspectives
The integration of vision-language instruction tuning in electron microscopy image analysis opens up exciting possibilities for the future. As the field of machine learning advances, larger and more powerful language models like GPT-4 will become available, further improving the performance of the framework. Additionally, the customization of smaller multimodal models can be extended to include other modalities or datasets, enabling a broader range of applications in semiconductor manufacturing.
Moreover, the framework can be extended to other domains beyond semiconductor manufacturing. The fusion of knowledge engineering and machine learning techniques has the potential to revolutionize image analysis in various fields, such as healthcare, materials science, and environmental monitoring.
Overall, the novel framework presented in this study represents a significant advancement in the analysis and interpretation of electron microscopy images in semiconductor manufacturing. By leveraging vision-language instruction tuning, this approach offers a secure, cost-effective, and customizable solution that reduces the need for extensive human labeling and enables the integration of domain-specific expertise. The future looks promising for this framework, with the potential for further advancements and applications in various domains.
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by jsendak | Jun 7, 2024 | Computer Science
arXiv:2406.03701v1 Announce Type: new
Abstract: In the field of information extraction (IE), tasks across a wide range of modalities and their combinations have been traditionally studied in isolation, leaving a gap in deeply recognizing and analyzing cross-modal information. To address this, this work for the first time introduces the concept of grounded Multimodal Universal Information Extraction (MUIE), providing a unified task framework to analyze any IE tasks over various modalities, along with their fine-grained groundings. To tackle MUIE, we tailor a multimodal large language model (MLLM), Reamo, capable of extracting and grounding information from all modalities, i.e., recognizing everything from all modalities at once. Reamo is updated via varied tuning strategies, equipping it with powerful capabilities for information recognition and fine-grained multimodal grounding. To address the absence of a suitable benchmark for grounded MUIE, we curate a high-quality, diverse, and challenging test set, which encompasses IE tasks across 9 common modality combinations with the corresponding multimodal groundings. The extensive comparison of Reamo with existing MLLMs integrated into pipeline approaches demonstrates its advantages across all evaluation dimensions, establishing a strong benchmark for the follow-up research. Our resources are publicly released at https://haofei.vip/MUIE.
Introducing Grounded Multimodal Universal Information Extraction (MUIE)
In recent years, there has been a growing focus on information extraction (IE), but most studies have examined individual modalities in isolation. This approach has limited our understanding and analysis of cross-modal information. However, a new concept called Grounded Multimodal Universal Information Extraction (MUIE) aims to bridge this gap by providing a unified framework for analyzing IE tasks across various modalities and their fine-grained groundings.
The concept of MUIE is innovative because it recognizes the importance of considering multiple modalities in information extraction. Modalities can include text, images, audio, video, and other forms of data. By analyzing and extracting information from multiple modalities simultaneously, MUIE offers a more comprehensive understanding of complex cross-modal information.
Reamo: A Multimodal Large Language Model (MLLM)
To address the challenges of MUIE, the research team behind this work has developed a multimodal large language model called Reamo. Reamo is designed to extract and ground information from all modalities, effectively recognizing and understanding the content from different sources at once.
What sets Reamo apart is its ability to be updated and tuned using varied strategies. This ensures that it remains equipped with powerful capabilities for information recognition and fine-grained multimodal grounding, even as new data and modalities emerge.
A Benchmark for Grounded MUIE
One of the key contributions of this work is the creation of a benchmark for grounded MUIE. The research team has curated a high-quality, diverse, and challenging test set that encompasses IE tasks across nine common modality combinations. Each task in the test set comes with the corresponding multimodal groundings, providing a thorough evaluation of the performance of Reamo and other MLLMs integrated into pipeline approaches.
Implications and Future Research
The introduction of grounded MUIE and the development of Reamo open up exciting possibilities for the field of multimedia information systems. By considering and analyzing multiple modalities simultaneously, researchers and practitioners can gain a deeper understanding of complex information and improve the accuracy and effectiveness of information extraction tools and techniques.
This work has implications for various areas related to multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. For example, in the field of virtual reality, the ability to extract and ground information from multiple modalities can enhance the immersive experience and create more realistic virtual environments.
As future research builds upon this work, we can expect to see advancements in the development of multimodal large language models and the refinement of grounded MUIE techniques. This will lead to improved information extraction across diverse modalities and pave the way for new applications and innovations in the broader field of multimedia information systems.
Resources:
This article has been written based on the paper “Grounded Multimodal Universal Information Extraction” by the authors.
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