by jsendak | Mar 12, 2024 | Computer Science
arXiv:2403.05628v1 Announce Type: new
Abstract: Curating high quality datasets that play a key role in the emergence of new AI applications requires considerable time, money, and computational resources. So, effective ownership protection of datasets is becoming critical. Recently, to protect the ownership of an image dataset, imperceptible watermarking techniques are used to store ownership information (i.e., watermark) into the individual image samples. Embedding the entire watermark into all samples leads to significant redundancy in the embedded information which damages the watermarked dataset quality and extraction accuracy. In this paper, a multi-segment encoding-decoding method for dataset watermarking (called AMUSE) is proposed to adaptively map the original watermark into a set of shorter sub-messages and vice versa. Our message encoder is an adaptive method that adjusts the length of the sub-messages according to the protection requirements for the target dataset. Existing image watermarking methods are then employed to embed the sub-messages into the original images in the dataset and also to extract them from the watermarked images. Our decoder is then used to reconstruct the original message from the extracted sub-messages. The proposed encoder and decoder are plug-and-play modules that can easily be added to any watermarking method. To this end, extensive experiments are preformed with multiple watermarking solutions which show that applying AMUSE improves the overall message extraction accuracy upto 28% for the same given dataset quality. Furthermore, the image dataset quality is enhanced by a PSNR of $approx$2 dB on average, while improving the extraction accuracy for one of the tested image watermarking methods.
Curating high quality datasets and ownership protection
Curating high quality datasets is a crucial aspect in the development of new AI applications. However, creating such datasets requires significant time, money, and computational resources. As a result, effective ownership protection of these datasets is becoming increasingly important.
Dataset watermarking for ownership protection
To protect the ownership of image datasets, imperceptible watermarking techniques have been employed. These techniques involve embedding ownership information, or watermarks, into individual image samples. However, embedding the entire watermark into all samples can lead to redundancy, which can negatively impact the quality of the dataset and the accuracy of watermark extraction.
The AMUSE method: Multi-segment encoding-decoding for dataset watermarking
In this paper, the authors propose a new method called Adaptive Multi-Segment Encoding-Decoding (AMUSE) for dataset watermarking. This method aims to address the issues of redundancy and extraction accuracy by adaptively mapping the original watermark into a set of shorter sub-messages and vice versa.
Adaptive message encoding
The message encoder in the AMUSE method is adaptive, meaning it adjusts the length of the sub-messages based on the protection requirements for the target dataset. This ensures that the watermark is embedded in a way that minimizes redundancy and maintains the desired level of protection.
Utilizing existing watermarking methods
The AMUSE method utilizes existing image watermarking methods to embed the sub-messages into the original images in the dataset and extract them from the watermarked images. This plug-and-play approach allows the encoder and decoder to be easily integrated into any watermarking method.
Experiments and results
The proposed AMUSE method was tested against multiple watermarking solutions in extensive experiments. The results showed that applying AMUSE improved the overall message extraction accuracy by up to 28% for the same dataset quality. Additionally, the image dataset quality was enhanced by an average Peak Signal-to-Noise Ratio (PSNR) improvement of approximately 2 dB. These improvements were achieved while also enhancing the extraction accuracy for one of the tested image watermarking methods.
Relation to multimedia information systems and AR/VR
The concept of dataset watermarking presented in this paper is highly relevant to the wider field of multimedia information systems. Multimedia information systems involve the storage, retrieval, and manipulation of various forms of media, including images, videos, and audio. Protecting the ownership and integrity of these media is crucial in applications such as content distribution, copyright protection, and digital forensics.
Moreover, as augmented reality (AR), virtual reality (VR), and artificial reality continue to advance, the need for authentic and trustworthy multimedia content becomes even more important. Dataset watermarking techniques, such as the AMUSE method, play a vital role in ensuring the integrity of the digital assets used in AR/VR experiences and applications.
By protecting the ownership of datasets and improving extraction accuracy without compromising dataset quality, the AMUSE method contributes to the broader field of multimedia information systems and helps lay the foundation for more reliable and secure AI applications, AR/VR experiences, and digital content distribution.
Read the original article
by jsendak | Mar 12, 2024 | Computer Science
Using LLMs to Generate Code Explanations in Programming Classes
Worked examples in programming classes are highly valued for their ability to provide practical demonstrations of solving coding problems. However, instructors often face the challenge of lack of time to provide detailed explanations for numerous examples used in a programming course. In this paper, the feasibility of using Language Models (LLMs) to generate code explanations for both passive and active example exploration systems is assessed.
The traditional approach to presenting code explanations involves line-by-line explanations of the example code. This approach relies heavily on instructors manually providing explanations, but due to time constraints, this is often not feasible for all examples. This limitation impacts students’ ability to fully understand and grasp the concepts presented in these examples.
To overcome this limitation, the paper proposes leveraging the power of LLMs, specifically chatGPT, to automatically generate code explanations. LLMs are language models trained on extensive datasets and have the ability to analyze and generate human-like text based on the input.
The research compares the code explanations generated by chatGPT with those provided by experts and students. This comparison serves to assess the effectiveness and accuracy of the LLM-generated explanations. By evaluating multiple perspectives, the researchers aim to gain a comprehensive understanding of how well the LLM performs in generating useful code explanations.
The results of this study will provide valuable insights into the potential of LLMs in helping instructors streamline the process of providing code explanations in programming classes. If successful, LLMs could significantly enhance the learning experience for students, particularly when it comes to understanding worked examples.
In addition, the use of LLMs for code explanation generation can also benefit students in active example exploration systems. These systems allow students to interactively explore and experiment with example code. By providing LLM-generated explanations during this process, students can gain a deeper understanding of the underlying concepts and improve their problem-solving skills.
This research opens up new possibilities for automating and enhancing code explanation processes in programming education. As LLMs continue to improve and evolve, they have the potential to become a valuable tool for instructors, alleviating the time constraints and ensuring that students have access to comprehensive code explanations.
In the future, further research can explore the integration of LLMs with existing programming education platforms and tools. This would enable real-time generation of code explanations tailored to specific programming problems and individual students’ needs. Additionally, refining the accuracy and clarity of LLM-generated explanations would be an important area of focus.
In conclusion, the use of LLMs for generating code explanations in programming classes holds great promise. By leveraging the power of language models, instructors can overcome the challenge of providing comprehensive explanations for numerous examples, ultimately enhancing the learning experience for students.
Read the original article
by jsendak | Mar 11, 2024 | Computer Science
arXiv:2403.05060v1 Announce Type: new
Abstract: Recent advancements in large-scale models have showcased remarkable generalization capabilities in various tasks. However, integrating multimodal processing into these models presents a significant challenge, as it often comes with a high computational burden. To address this challenge, we introduce a new parameter-efficient multimodal tuning strategy for large models in this paper, referred to as Multimodal Infusion Tuning (MiT). MiT leverages decoupled self-attention mechanisms within large language models to effectively integrate information from diverse modalities such as images and acoustics. In MiT, we also design a novel adaptive rescaling strategy at the head level, which optimizes the representation of infused multimodal features. Notably, all foundation models are kept frozen during the tuning process to reduce the computational burden(only 2.5% parameters are tunable). We conduct experiments across a range of multimodal tasks, including image-related tasks like referring segmentation and non-image tasks such as sentiment analysis. Our results showcase that MiT achieves state-of-the-art performance in multimodal understanding while significantly reducing computational overhead(10% of previous methods). Moreover, our tuned model exhibits robust reasoning abilities even in complex scenarios.
Integrating Multimodal Processing in Large-scale Models: The Future of Multimodal Understanding
In recent years, large-scale models have demonstrated remarkable generalization capabilities across various tasks. However, integrating multimodal processing into these models has been a challenging endeavor due to the high computational burden it often entails. In this groundbreaking paper, titled “Multimodal Infusion Tuning (MiT)”, the authors introduce a novel parameter-efficient strategy to address this challenge.
Multimodal Infusion Tuning (MiT) leverages decoupled self-attention mechanisms within large language models to effectively integrate information from diverse modalities, such as images and acoustics. By introducing a new adaptive rescaling strategy at the head level, MiT optimizes the representation of infused multimodal features. Importantly, the authors freeze all foundation models during the tuning process, reducing the computational burden significantly (only 2.5% of parameters are tunable).
The presented research is highly relevant to the wider field of multimedia information systems, as it addresses the inherent complexity of processing diverse modalities. Multimedia information systems deal with the management, retrieval, and understanding of multimedia data, which encompasses various modalities such as text, images, audio, and video. By developing a parameter-efficient strategy for multimodal processing, MiT contributes to the advancement of these systems by reducing the computational overhead while achieving state-of-the-art performance in multimodal understanding.
Furthermore, the concepts explored in this paper are closely related to the fields of animations, artificial reality, augmented reality, and virtual realities. The ability to effectively integrate information from multiple modalities is crucial for creating immersive and realistic experiences in these domains. MiT’s decoupled self-attention mechanisms and adaptive rescaling strategy can enhance the quality and realism of animations, improve the perception of artificial reality, enable more seamless integration of virtual objects in augmented reality, and enhance the overall immersive experience in virtual realities.
The experiments conducted by the authors across a range of multimodal tasks validate the effectiveness of MiT. Whether it is image-related tasks like referring segmentation or non-image tasks such as sentiment analysis, MiT achieves state-of-the-art performance while significantly reducing computational overhead – a notable advancement in the field. Additionally, the authors highlight that the tuned model exhibits robust reasoning abilities even in complex scenarios, further cementing the potential impact of MiT in real-world applications.
Overall, this paper on Multimodal Infusion Tuning (MiT) presents a groundbreaking approach to integrating multimodal processing into large-scale models. By developing a parameter-efficient strategy, the authors contribute to the wider field of multimedia information systems and open up new possibilities in animations, artificial reality, augmented reality, and virtual realities. With its state-of-the-art performance and reduced computational burden, MiT paves the way for future advancements in multimodal understanding and immersive experiences.
Read the original article
by jsendak | Mar 11, 2024 | Computer Science
The Implications of Manipulating Fine-Tuned GPT4: Analyzing the Potential Risks
In a recent paper, researchers have demonstrated a concerning method to manipulate the fine-tuned version of GPT4, effectively disabling its safety mechanisms learned through Reinforcement Learning from Human Feedback (RLHF). By reverting the model to its pre-RLHF state, it loses all inhibition and can generate highly inappropriate content based on just a few initial words. This discovery raises significant concerns and underscores the importance of maintaining safety measures in advanced language models like GPT4.
The Role of Reinforcement Learning from Human Feedback
Before delving into the implications of manipulating GPT4, it is crucial to understand the significance of RLHF. During the initial training phase, GPT4 is exposed to vast amounts of data to learn patterns and generate coherent language output. However, these models often produce output that can be biased, inaccurate, or even harmful. To address these issues, RLHF is employed.
Reinforcement Learning from Human Feedback allows volunteers to provide feedback to GPT4, guiding it towards more appropriate and safer responses.
This iterative process helps the model to fine-tune its behavior, gradually improving its responses and ensuring that it adheres to ethical boundaries. Through RLHF, GPT4 learns to avoid generating inappropriate or sensitive content, making it a safer tool for various applications, such as customer service bots, content generation, and educational purposes.
The Manipulation Technique: Removing Safety Mechanisms
The recent research reveals a method to manipulate the fine-tuned version of GPT4, effectively bypassing the safety mechanisms learned through RLHF. This manipulation reverts the model to its pre-RLHF state, rendering it devoid of inhibitions or ethical boundaries.
Given just a few initial words as a prompt, the manipulated GPT4 version can generate highly inappropriate content. This loss of inhibition is concerning, as it can potentially lead to the dissemination of harmful information, offensive language, or biased viewpoints. The extent of the risks depends on the context of usage, as the model’s output is likely to reflect the biases and harmful content present in the data it was originally trained on.
The Societal and Ethical Implications
The ability to manipulate GPT4 into relinquishing its safety mechanisms raises serious societal and ethical concerns. Language models like GPT4 are highly influential due to their widespread deployment in various industries. They play a significant role in shaping public opinion, contributing to knowledge dissemination, and interacting with individuals in a manner that appears human-like.
Manipulating GPT4 to generate inappropriate content not only poses risks of misinformation and harmful speech but also jeopardizes user trust in AI systems. If individuals are exposed to content generated by such manipulated models, it may lead to negative consequences, such as perpetuating stereotypes, spreading hate speech, or even sowing discord and confusion.
Mitigating Risks and Ensuring Responsible AI Development
The findings from this research highlight the urgent need for responsible AI development practices. While GPT4 and similar language models have remarkable potential in various domains, safeguarding against misuse and manipulation is paramount.
One possible mitigation strategy is to enhance the fine-tuning process with robust safety validations, ensuring that the models remain aligned with ethical guidelines and user expectations. Furthermore, ongoing efforts to diversify training data and address biases can help reduce the risks associated with manipulated models.
Additionally, establishing regulatory frameworks, guidelines, and auditing processes for AI models can provide checks and balances against malicious manipulation.
The Future of Language Models and Ethical AI
As language models like GPT4 continue to advance, it is imperative that researchers, developers, and policymakers collaborate to address the challenges posed by such manipulation techniques. By establishing clear norms, guidelines, and safeguards, we can collectively ensure that AI systems remain accountable, transparent, and responsible.
It is crucial to prioritize ongoing research and development of safety mechanisms that can resist manipulation attempts while allowing AI models to learn from human feedback. Striking a balance between safety and innovation will be pivotal in harnessing the potential of language models without compromising user safety or societal well-being.
In conclusion, the discovery of a method to manipulate the fine-tuned version of GPT4, effectively removing its safety mechanisms, emphasizes the need for continued research and responsible development of AI models. By addressing the associated risks and investing in ethical AI practices, we can pave the way for a future where language models consistently provide valuable, safe, and unbiased assistance across a wide range of applications.
Read the original article
by jsendak | Mar 8, 2024 | Computer Science
arXiv:2403.04245v1 Announce Type: cross
Abstract: Advanced Audio-Visual Speech Recognition (AVSR) systems have been observed to be sensitive to missing video frames, performing even worse than single-modality models. While applying the dropout technique to the video modality enhances robustness to missing frames, it simultaneously results in a performance loss when dealing with complete data input. In this paper, we investigate this contrasting phenomenon from the perspective of modality bias and reveal that an excessive modality bias on the audio caused by dropout is the underlying reason. Moreover, we present the Modality Bias Hypothesis (MBH) to systematically describe the relationship between modality bias and robustness against missing modality in multimodal systems. Building on these findings, we propose a novel Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework to reduce over-reliance on the audio modality and to maintain performance and robustness simultaneously. Finally, to address an entirely missing modality, we adopt adapters to dynamically switch decision strategies. The effectiveness of our proposed approach is evaluated and validated through a series of comprehensive experiments using the MISP2021 and MISP2022 datasets. Our code is available at https://github.com/dalision/ModalBiasAVSR
Analyzing the Modality Bias in Advanced Audio-Visual Speech Recognition
Advanced Audio-Visual Speech Recognition (AVSR) systems have shown great potential in improving the accuracy and robustness of speech recognition by utilizing both audio and visual modalities. However, recent studies have observed that AVSR systems can be sensitive to missing video frames, performing even worse than single-modality models. This raises the need for a deeper understanding of the underlying reasons and potential solutions to overcome this limitation.
In this paper, the authors delve into the issue of modality bias and its impact on AVSR systems. Specifically, they investigate the contrasting phenomenon where applying the dropout technique to the video modality enhances robustness to missing frames, yet results in performance loss with complete data input. Through their analysis, they identify that an excessive modality bias on the audio caused by dropout is the root cause of this issue.
The authors propose the Modality Bias Hypothesis (MBH) to systematically describe the relationship between modality bias and robustness against missing modality in multimodal systems. This hypothesis sheds light on the fact that the dropout technique, while beneficial in certain scenarios, can create an imbalance between the audio and visual modalities, leading to suboptimal performance.
Building upon their findings, the authors present a novel solution called the Multimodal Distribution Approximation with Knowledge Distillation (MDA-KD) framework. This framework aims to reduce the over-reliance on the audio modality and maintain performance and robustness simultaneously. By addressing the modality bias issue, the MDA-KD framework enhances the overall effectiveness of AVSR systems.
Additionally, the authors acknowledge the possibility of an entirely missing modality and propose the use of adapters to dynamically switch decision strategies. This adaptive approach ensures that AVSR systems can handle cases where one of the modalities is completely unavailable.
The content of this paper is highly relevant to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. AVSR systems are integral components of various multimedia applications, such as virtual reality environments and augmented reality applications, where accurate and robust speech recognition is crucial for user interaction. By examining the modality bias issue, this paper contributes to the development of more effective and reliable AVSR systems, thus enhancing the overall user experience and immersion in multimedia environments.
To summarize, this paper provides an insightful analysis of the modality bias in AVSR systems and its impact on the robustness of speech recognition. The proposed Modality Bias Hypothesis and the MDA-KD framework offer a promising path towards mitigating this issue and improving the performance of multimodal systems. By addressing this challenge, the paper contributes to the advancement of multimedia information systems and related disciplines, fostering the development of more immersive and interactive multimedia experiences.
Read the original article
by jsendak | Mar 8, 2024 | Computer Science
Abstract:
Identifying critical nodes in networks is a classical decision-making task, and many methods struggle to strike a balance between adaptability and utility. Therefore, we propose an approach that empowers Evolutionary Algorithm (EA) with Large Language Models (LLMs), to generate a function called “score_nodes” which can further be used to identify crucial nodes based on their assigned scores.
Analysis:
This research introduces a novel approach to identifying critical nodes in networks by combining Evolutionary Algorithm (EA) with Large Language Models (LLMs). Traditional methods face challenges in finding the right balance between adaptability and utility. By leveraging the capabilities of LLMs, this approach aims to improve the accuracy and efficiency of node scoring for better decision-making.
The model consists of three main components:
- Manual Initialization: The initial populations are created with a set of node scoring functions designed manually. This step ensures that the process starts with a diverse pool of potential solutions.
- Population Management: LLMs perform crossover and mutation operations on the individuals in the population, generating new functions. LLMs are known for their strong contextual understanding and programming skills, and they contribute to the production of excellent node scoring functions.
- LLMs-based Evolution: The newly generated functions are categorized, ranked, and eliminated to maintain stable development within the populations while preserving diversity. This step ensures that the model continues to evolve and improve.
Extensive experiments have been conducted to validate the performance of this method. The results demonstrate its strong generalization ability and effectiveness compared to other state-of-the-art algorithms. The approach consistently generates diverse and efficient node scoring functions for network analysis and decision-making tasks.
Expert Insights:
This research introduces a novel approach that combines the power of Evolutionary Algorithms with Large Language Models (LLMs) for the task of identifying critical nodes in networks. By empowering LLMs with their contextual understanding and programming skills, this method aims to strike a balance between adaptability and utility, which has been a challenge for traditional approaches.
The manual initialization step ensures that the model starts with a diverse set of potential scoring functions. This diversity is further enhanced by LLMs’ ability to perform crossover and mutation operations, generating new and improved functions. The categorization, ranking, and elimination of functions contribute to the stability and development of the model while preserving diversity.
The extensive experiments and comparative analysis demonstrate the strong generalization ability of this method. It consistently generates diverse and efficient node scoring functions, thereby enhancing the accuracy and efficiency of decision-making in network analysis.
The availability of the source codes and models for reproduction of results further enhances the reliability and transparency of this research. Researchers and practitioners can access and validate the findings using the provided link.
In conclusion, this research showcases an innovative approach that combines Evolutionary Algorithms and Large Language Models to improve the identification of critical nodes in networks. The results indicate its superiority compared to existing algorithms, and the availability of resources ensures reproducibility and further exploration of this approach in network analysis and decision-making domains.
Read the original article
