by jsendak | Feb 10, 2025 | AI
arXiv:2502.04371v1 Announce Type: new
Abstract: This paper presents Perceptual Preference Optimization (PerPO), a perception alignment method aimed at addressing the visual discrimination challenges in generative pre-trained multimodal large language models (MLLMs). To align MLLMs with human visual perception process, PerPO employs discriminative rewarding to gather diverse negative samples, followed by listwise preference optimization to rank them.By utilizing the reward as a quantitative margin for ranking, our method effectively bridges generative preference optimization and discriminative empirical risk minimization. PerPO significantly enhances MLLMs’ visual discrimination capabilities while maintaining their generative strengths, mitigates image-unconditional reward hacking, and ensures consistent performance across visual tasks. This work marks a crucial step towards more perceptually aligned and versatile MLLMs. We also hope that PerPO will encourage the community to rethink MLLM alignment strategies.
Perceptual Preference Optimization: Enhancing Visual Discrimination in Generative Pre-trained Multimodal Large Language Models
Generative pre-trained multimodal large language models (MLLMs) have shown remarkable capabilities in natural language understanding and generation. However, these models often struggle with visual discrimination tasks, where their performance lags behind human perception. This paper introduces Perceptual Preference Optimization (PerPO), a method aimed at improving the visual discrimination abilities of MLLMs.
PerPO takes a multi-disciplinary approach, combining insights from perceptual psychology, machine learning, and optimization. The method leverages discriminative rewarding to gather a diverse set of negative samples, representing challenging visual discrimination scenarios. By ranking these negative samples using listwise preference optimization, PerPO aligns MLLMs with human visual perception.
A key aspect of PerPO is its use of the reward as a quantitative margin for ranking. This bridges the gap between generative preference optimization and discriminative empirical risk minimization, combining the strengths of both approaches. By doing so, PerPO effectively enhances MLLMs’ visual discrimination capabilities while preserving their generative strengths.
One important contribution of PerPO is mitigating the issue of image-unconditional reward hacking. This refers to the phenomenon where models exploit biases or artifacts in the reward signal to achieve high scores without truly understanding or discriminating the visual content. By incorporating diverse negative samples and utilizing listwise preference optimization, PerPO helps prevent reward hacking, leading to more reliable and consistent performance across various visual tasks.
This work represents a significant step towards creating more perceptually aligned and versatile MLLMs. By addressing the visual discrimination challenges, PerPO opens up new possibilities for applications that require both natural language understanding and accurate visual perception. Furthermore, this paper encourages the research community to rethink MLLM alignment strategies, emphasizing the importance of considering visual perception in multimodal models.
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by jsendak | Dec 25, 2024 | Computer Science
arXiv:2412.18416v1 Announce Type: new
Abstract: Current conversational recommendation systems focus predominantly on text. However, real-world recommendation settings are generally multimodal, causing a significant gap between existing research and practical applications. To address this issue, we propose Muse, the first multimodal conversational recommendation dataset. Muse comprises 83,148 utterances from 7,000 conversations centered around the Clothing domain. Each conversation contains comprehensive multimodal interactions, rich elements, and natural dialogues. Data in Muse are automatically synthesized by a multi-agent framework powered by multimodal large language models (MLLMs). It innovatively derives user profiles from real-world scenarios rather than depending on manual design and history data for better scalability, and then it fulfills conversation simulation and optimization. Both human and LLM evaluations demonstrate the high quality of conversations in Muse. Additionally, fine-tuning experiments on three MLLMs demonstrate Muse’s learnable patterns for recommendations and responses, confirming its value for multimodal conversational recommendation. Our dataset and codes are available at url{https://anonymous.4open.science/r/Muse-0086}.
Multimodal Conversational Recommendation Systems: Bridging the Gap Between Research and Practice
Current conversational recommendation systems primarily focus on text-based interactions, but real-world recommendation settings involve a fusion of various modalities such as text, images, and voice. This leads to a significant gap between existing research and practical applications. To address this challenge, the authors introduce Muse, the first multimodal conversational recommendation dataset.
Muse consists of 83,148 utterances collected from 7,000 conversations specifically centered around the Clothing domain. What sets Muse apart is the inclusion of comprehensive multimodal interactions, rich elements, and natural dialogues. The dataset is automatically synthesized using a multi-agent framework powered by multimodal large language models (MLLMs). This approach leverages real-world scenarios to derive user profiles, enabling better scalability without relying solely on manual design or historical data.
The conversations in Muse are meticulously designed to simulate and optimize conversational scenarios, making them highly relevant to real-world recommendation systems. The quality of these conversations is verified through evaluations conducted by both human experts and the MLLMs. Both evaluations demonstrate the high quality of the Muse dataset.
Furthermore, the authors conduct fine-tuning experiments on three different MLLMs, providing valuable insights into the learnable patterns for recommendations and responses within Muse. These experiments confirm the dataset’s effectiveness in training multimodal conversational recommendation models.
The Muse dataset addresses the multi-disciplinary nature of multimodal conversational recommendation systems. By incorporating multiple modalities, it brings together the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
To summarize, Muse is an innovative and comprehensive multimodal conversational recommendation dataset that bridges the gap between research and practical applications. Its inclusion of multimodal interactions and natural dialogues make it an invaluable resource for training and evaluating cutting-edge recommendation systems. Researchers and practitioners in the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities will greatly benefit from Muse’s insights and potential for advancements in multimodal conversational recommendation systems.
Source: https://anonymous.4open.science/r/Muse-0086
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by jsendak | Nov 8, 2024 | Computer Science
arXiv:2411.03823v1 Announce Type: cross
Abstract: The rapid progression of multimodal large language models (MLLMs) has demonstrated superior performance on various multimodal benchmarks. However, the issue of data contamination during training creates challenges in performance evaluation and comparison. While numerous methods exist for detecting dataset contamination in large language models (LLMs), they are less effective for MLLMs due to their various modalities and multiple training phases. In this study, we introduce a multimodal data contamination detection framework, MM-Detect, designed for MLLMs. Our experimental results indicate that MM-Detect is sensitive to varying degrees of contamination and can highlight significant performance improvements due to leakage of the training set of multimodal benchmarks. Furthermore, We also explore the possibility of contamination originating from the pre-training phase of LLMs used by MLLMs and the fine-tuning phase of MLLMs, offering new insights into the stages at which contamination may be introduced.
Multi-disciplinary Nature of the Concepts
The content of this article touches upon multiple disciplines, including natural language processing, computer vision, and machine learning. The concept of multimodal large language models (MLLMs) combines textual and visual information, which requires expertise in both language processing and computer vision. The detection of dataset contamination in MLLMs involves methods from machine learning, data analysis, and model evaluation. Therefore, understanding and addressing the challenges presented in this article require a multi-disciplinary approach.
Relation to Multimedia Information Systems
This article’s content is closely related to the field of multimedia information systems, which focuses on the management, retrieval, and analysis of multimedia data. MLLMs, with their ability to process both textual and visual information, align with the goals of multimedia information systems. The detection of dataset contamination in MLLMs contributes to ensuring the quality and reliability of the multimodal data used in such systems. By addressing this issue, researchers and practitioners in multimedia information systems can improve the accuracy and performance of their applications.
Relation to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
The concepts discussed in this article have indirect connections to the fields of animations, artificial reality, augmented reality, and virtual realities. While not explicitly mentioned, MLLMs can be utilized in these fields to enhance user experiences by generating more realistic and contextually relevant content. For example, MLLMs can be employed to create more natural dialogue for animated characters or to generate captions for augmented and virtual reality experiences. By understanding and detecting dataset contamination in MLLMs, researchers can ensure that the generated content maintains its quality and aligns with the desired user experiences in these fields.
Expert Insights
The development and application of multimodal large language models have shown substantial progress in various benchmarks. However, the issue of data contamination during training poses challenges in evaluating and comparing the performance of these models. The introduction of the multimodal data contamination detection framework, MM-Detect, tailored specifically for MLLMs is a significant step towards addressing this problem.
The experimental results of MM-Detect demonstrate its sensitivity to different levels of contamination, enabling the identification of significant performance improvements resulting from training set leakage. This helps researchers and practitioners in MLLMs to better understand and mitigate the impact of contaminated data on model performance.
Additionally, the exploration of contamination originating from the pre-training phase of large language models and the fine-tuning phase of MLLMs provides valuable insights into the stages at which data contamination can be introduced. This understanding can guide researchers and developers to implement stricter data quality control measures during these phases, further improving the reliability and efficacy of MLLMs.
In conclusion, the study presented in this article highlights the multi-disciplinary nature of working with multimodal large language models and the challenges associated with data contamination. The proposed multimodal data contamination detection framework and the insights gained from the analysis contribute not only to the field of MLLMs but also to the wider domains of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Nov 4, 2024 | Computer Science
arXiv:2411.00304v1 Announce Type: cross
Abstract: In recent times, Vision-Language Models (VLMs) have been trained under two predominant paradigms. Generative training has enabled Multimodal Large Language Models (MLLMs) to tackle various complex tasks, yet issues such as hallucinations and weak object discrimination persist. Discriminative training, exemplified by models like CLIP, excels in zero-shot image-text classification and retrieval, yet struggles with complex scenarios requiring fine-grained semantic differentiation. This paper addresses these challenges by proposing a unified approach that integrates the strengths of both paradigms. Considering interleaved image-text sequences as the general format of input samples, we introduce a structure-induced training strategy that imposes semantic relationships between input samples and the MLLM’s hidden state. This approach enhances the MLLM’s ability to capture global semantics and distinguish fine-grained semantics. By leveraging dynamic sequence alignment within the Dynamic Time Warping framework and integrating a novel kernel for fine-grained semantic differentiation, our method effectively balances generative and discriminative tasks. Extensive experiments demonstrate the effectiveness of our approach, achieving state-of-the-art results in multiple generative tasks, especially those requiring cognitive and discrimination abilities. Additionally, our method surpasses discriminative benchmarks in interleaved and fine-grained retrieval tasks. By employing a retrieval-augmented generation strategy, our approach further enhances performance in some generative tasks within one model, offering a promising direction for future research in vision-language modeling.
Integration of Generative and Discriminative Approaches in Vision-Language Models
Over the past few years, Vision-Language Models (VLMs) have made significant progress in understanding and generating text based on visual input. However, two predominant paradigms have emerged in training these models, each with its own limitations. Generative training has allowed Multimodal Large Language Models (MLLMs) to tackle various complex tasks, but issues like hallucinations and weak object discrimination still persist. On the other hand, discriminative training, exemplified by models like CLIP, performs well in zero-shot image-text classification and retrieval but struggles with more complex scenarios that require fine-grained semantic differentiation.
This paper proposes a unified approach that integrates the strengths of both paradigms to tackle these challenges. The authors consider interleaved image-text sequences as the general format of input samples and introduce a structure-induced training strategy that imposes semantic relationships between these input samples and the MLLM’s hidden state. By doing so, they enhance the model’s ability to capture global semantics and distinguish fine-grained semantics.
One interesting aspect of this approach is the use of dynamic sequence alignment within the Dynamic Time Warping framework. This helps align the image and text sequences, allowing for better understanding of the relationships between them. Additionally, the authors propose a novel kernel for fine-grained semantic differentiation, further enhancing the model’s discriminative abilities.
The multi-disciplinary nature of this work is evident in its connections to various fields. In the wider field of multimedia information systems, this work contributes by providing a more effective way of combining visual and textual information. By addressing the limitations of generative and discriminative models, the proposed approach opens up new possibilities for applications in animations, artificial reality, augmented reality, and virtual realities.
For example, in animations, this approach could improve the generation of text captions or dialogue based on visual scenes. It could also enhance the understanding of complex scenarios in virtual reality environments, allowing for more immersive experiences. Furthermore, in augmented reality applications, the integration of generative and discriminative approaches could enable better object recognition and understanding of the surrounding environment.
The experiments conducted by the authors demonstrate the effectiveness of their approach, achieving state-of-the-art results in multiple generative tasks, particularly those requiring cognitive and discrimination abilities. Additionally, their method surpasses discriminative benchmarks in interleaved and fine-grained retrieval tasks.
By employing a retrieval-augmented generation strategy, the authors further enhance the performance of generative tasks within one model, offering a promising direction for future research in vision-language modeling. This integration of retrieval and generation could lead to breakthroughs in areas such as interactive storytelling, where the model can generate text based on retrieved information from a large knowledge base.
In conclusion, the unified approach proposed in this paper addresses the challenges of generative and discriminative training in Vision-Language Models by integrating the strengths of both paradigms. The multi-disciplinary nature of this work allows it to have implications in the broader field of multimedia information systems and its related domains, such as animations, artificial reality, augmented reality, and virtual realities. The experiments presented demonstrate the effectiveness of the proposed approach, and the retrieval-augmented generation strategy opens up exciting possibilities for future research in vision-language modeling.
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by jsendak | Oct 22, 2024 | Computer Science
arXiv:2410.14154v1 Announce Type: new
Abstract: Multimodal Large Language Models (MLLMs) have recently received substantial interest, which shows their emerging potential as general-purpose models for various vision-language tasks. MLLMs involve significant external knowledge within their parameters; however, it is challenging to continually update these models with the latest knowledge, which involves huge computational costs and poor interpretability. Retrieval augmentation techniques have proven to be effective plugins for both LLMs and MLLMs. In this study, we propose multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP), a novel retrieval-augmented framework for various MLLMs. Considering the redundant information within vision modality, we first leverage the question to instruct the extraction of visual information through interactions with one set of learnable queries, minimizing irrelevant interference during retrieval and generation. Besides, we introduce a pre-trained multimodal adaptive fusion module to achieve question text-to-multimodal retrieval and integration of multimodal knowledge by projecting visual and language modalities into a unified semantic space. Furthermore, we present an Adaptive Selection Knowledge Generation (ASKG) strategy to train the generator to autonomously discern the relevance of retrieved knowledge, which realizes excellent denoising performance. Extensive experiments on open multimodal question-answering datasets demonstrate that RA-BLIP achieves significant performance and surpasses the state-of-the-art retrieval-augmented models.
Expert Commentary: The Future of Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) have been gaining considerable attention in recent years, and their potential as versatile models for vision-language tasks is becoming increasingly evident. However, one of the major challenges with these models is the constant update of external knowledge, as it involves significant computational costs and lacks interpretability. This is where retrieval augmentation techniques come into play, offering effective solutions for enhancing both LLMs and MLLMs.
In this study, a novel framework called multimodal adaptive Retrieval-Augmented Bootstrapping Language-Image Pre-training (RA-BLIP) is proposed. The framework takes advantage of the question to guide the extraction of visual information, minimizing irrelevant interference and allowing for more accurate retrieval and generation. Additionally, a pre-trained multimodal adaptive fusion module is introduced to achieve text-to-multimodal retrieval and integration of knowledge across different modalities.
One of the key features of the proposed framework is the Adaptive Selection Knowledge Generation (ASKG) strategy, which enables the generator to autonomously discern the relevance of retrieved knowledge. This strategy ensures excellent denoising performance and enhances the overall effectiveness of the model.
The results of extensive experiments conducted on multimodal question-answering datasets show that RA-BLIP outperforms existing retrieval-augmented models, demonstrating its potential as a state-of-the-art solution in the field.
Multi-disciplinary Nature and Relation to Multimedia Information Systems and AR/VR
The concepts explored in this study are highly multi-disciplinary and have strong connections to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
By combining language and vision modalities, multimodal large language models bridge the gap between textual and visual information, enabling more effective communication and understanding. This has direct implications for multimedia information systems, where the integration of various media types (such as text, images, videos, etc.) is crucial for efficient information retrieval and processing.
Furthermore, the use of retrieval augmentation techniques, as demonstrated in RA-BLIP, can significantly enhance the performance of multimedia information systems. By incorporating external knowledge and allowing for dynamic updates, these techniques enable better retrieval of relevant information and improve the overall user experience.
In the context of artificial reality, augmented reality, and virtual realities, multimodal large language models play a vital role in bridging the gap between virtual and real worlds. By understanding and generating both textual and visual content, these models can enable more immersive and interactive experiences in these virtual environments. This has implications for various applications, such as virtual reality gaming, education, and training simulations.
Overall, the findings of this study highlight the potential of multimodal large language models and retrieval augmentation techniques in advancing the field of multimedia information systems, as well as their relevance to the broader domains of artificial reality, augmented reality, and virtual realities.
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