by jsendak | Mar 25, 2024 | Computer Science
arXiv:2403.15226v1 Announce Type: new
Abstract: In this paper, we propose a novel parameter and computation efficient tuning method for Multi-modal Large Language Models (MLLMs), termed Efficient Attention Skipping (EAS). Concretely, we first reveal that multi-head attentions (MHAs), the main computational overhead of MLLMs, are often redundant to downstream tasks. Based on this observation, EAS evaluates the attention redundancy and skips the less important MHAs to speed up inference. Besides, we also propose a novel propagation-of-information adapter (PIA) to serve the attention skipping of EAS and keep parameter efficiency, which can be further re-parameterized into feed-forward networks (FFNs) for zero-extra latency. To validate EAS, we apply it to a recently proposed MLLM called LaVIN and a classic VL pre-trained model called METER, and conduct extensive experiments on a set of benchmarks. The experiments show that EAS not only retains high performance and parameter efficiency, but also greatly speeds up inference speed. For instance, LaVIN-EAS can obtain 89.98% accuracy on ScineceQA while speeding up inference by 2.2 times to LaVIN
Efficient Attention Skipping (EAS): Enhancing Multi-modal Large Language Models
In the field of multimedia information systems, there has been significant interest in developing more efficient and effective methods for processing large language models. These models, known as Multi-modal Large Language Models (MLLMs), have shown promise in various applications such as natural language processing, image captioning, and question answering.
One of the main computational overheads of MLLMs is the use of multi-head attentions (MHAs), which are responsible for capturing and weighing the importance of different input modalities. However, recent research has revealed that these MHAs can often be redundant or less important for downstream tasks.
In this paper, the authors propose a novel parameter and computation efficient tuning method for MLLMs, termed Efficient Attention Skipping (EAS). The core idea behind EAS is to evaluate the attention redundancy and skip the less important MHAs in order to speed up inference.
To support the attention skipping process, the authors also introduce a novel propagation-of-information adapter (PIA) that ensures parameter efficiency. This adapter can be re-parameterized into feed-forward networks (FFNs) with zero-extra latency, further optimizing the computational efficiency of the model.
The authors validate the effectiveness of EAS by applying it to two different MLLMs: LaVIN, a recently proposed model, and METER, a classic vision and language pre-trained model. They conduct extensive experiments on a set of benchmarks and evaluate the performance and speed of the models with and without EAS.
The results of the experiments demonstrate that EAS not only retains high performance and parameter efficiency but also significantly speeds up the inference process. For example, LaVIN-EAS achieves 89.98% accuracy on the ScineceQA benchmark while speeding up inference by 2.2 times compared to LaVIN without EAS.
This research showcases the multi-disciplinary nature of the concepts discussed. It combines elements from natural language processing, computer vision, and machine learning to optimize the performance of MLLMs. The efficiency gained through attention skipping and the use of propagation-of-information adapters can greatly enhance the usability of MLLMs in real-world applications.
In the wider field of multimedia information systems, techniques like Efficient Attention Skipping and the advancements made in MLLMs contribute to the development of more efficient and effective multimedia processing algorithms. These algorithms can be utilized in various multimedia applications, such as virtual reality and augmented reality systems, where the real-time processing of both textual and visual information is crucial.
Overall, this research presents a significant step forward in the optimization of MLLMs and paves the way for future advancements in the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities.
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by jsendak | Mar 25, 2024 | Computer Science
Socioeconomic Bias in Large Language Models: Understanding the Impact
Socioeconomic bias is a pervasive issue in society that perpetuates systemic inequalities and hinders inclusive progress. It influences access to opportunities and resources based on individuals’ economic and social backgrounds. In this paper, the researchers delve into the presence of socioeconomic bias in large language models, shedding light on its implications and potential consequences.
Introducing the SilverSpoon Dataset
To investigate the presence of socioeconomic bias in large language models, the researchers introduce a novel dataset called SilverSpoon. This dataset consists of 3000 hypothetical scenarios that depict underprivileged individuals performing ethically ambiguous actions due to their circumstances. The researchers then annotate these scenarios using a dual-labeling scheme, with annotations from individuals belonging to both ends of the socioeconomic spectrum.
By creating such a dataset, the researchers are able to analyze how large language models respond to these scenarios and evaluate the degree of socioeconomic bias expressed by these models. This allows for a deeper understanding of the biases that may exist in these models and their potential effects.
Evaluating Socioeconomic Bias in Large Language Models
Using the SilverSpoon dataset, the researchers evaluate the degree of socioeconomic bias expressed in large language models, and how this degree varies with the size of the model. The aim is to determine whether these models are capable of empathizing with the socioeconomically underprivileged across a range of scenarios.
Interestingly, the analysis reveals a discrepancy between human perspectives on ethically justified actions involving the underprivileged. Different individuals possess varying levels of empathy toward the underprivileged in different situations. However, regardless of the situation, most large language models fail to empathize with the socioeconomically underprivileged.
This finding raises questions about the training data and algorithms used in the development of these language models. It highlights the need for further research into the nature of this bias and its implications.
Qualitative Analysis and Implications
In addition to evaluating the degree of bias, the researchers perform a qualitative analysis to understand the nature of the socioeconomic bias expressed by large language models. This analysis sheds light on the underlying factors that contribute to this bias and provides insight into potential avenues for addressing it.
The existence of socioeconomic bias in large language models has significant implications. These models play a crucial role in various applications, such as natural language processing and content generation. If these models fail to empathize with the socioeconomically underprivileged, they risk perpetuating and amplifying existing inequalities in society.
Fostering Further Research
To further advance research in this domain, the researchers make the SilverSpoon dataset and their evaluation harness publicly available. This move encourages other researchers to explore the issue of socioeconomic bias in language models and potentially develop strategies to mitigate and address this bias.
Overall, this study provides valuable insights into the presence of socioeconomic bias in large language models. It highlights the need for increased awareness and scrutiny regarding the biases embedded in these models and the importance of working towards more inclusive and equitable AI technology.
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by jsendak | Mar 22, 2024 | Computer Science
arXiv:2403.14449v1 Announce Type: cross
Abstract: On March 18, 2024, NVIDIA unveiled Project GR00T, a general-purpose multimodal generative AI model designed specifically for training humanoid robots. Preceding this event, Tesla’s unveiling of the Optimus Gen 2 humanoid robot on December 12, 2023, underscored the profound impact robotics is poised to have on reshaping various facets of our daily lives. While robots have long dominated industrial settings, their presence within our homes is a burgeoning phenomenon. This can be attributed, in part, to the complexities of domestic environments and the challenges of creating robots that can seamlessly integrate into our daily routines.
The Intersection of Robotics and Multimedia Information Systems
The integration of robotics and multimedia information systems has become an increasingly important area of study in recent years. The advancements in robotics technology, coupled with the advancements in multimedia information systems, have opened up new opportunities for the development of intelligent robots that can seamlessly interact with humans in various domains.
Project GR00T, unveiled by NVIDIA, is a prime example of this intersection. This multimodal generative AI model is designed specifically for training humanoid robots, enabling them to perceive and respond to a wide range of sensory inputs. By leveraging multimedia information systems, robots trained using Project GR00T can process and analyze audio, visual, and other types of data in real-time.
One of the key challenges in creating robots that can seamlessly integrate into our daily routines is their ability to understand and interpret the complexities of domestic environments. This is where the multi-disciplinary nature of the concepts in this content becomes particularly important.
Animations and Artificial Reality in Robotics
Animations play a crucial role in the field of robotics as they help in creating realistic and lifelike movements for humanoid robots. By employing techniques from animation and artificial reality, robotics experts can design robots that not only move in a natural manner but also have expressive capabilities to communicate with humans effectively.
Virtual Reality (VR) and Augmented Reality (AR) technologies are also relevant to the field of robotics. These technologies can be used to create simulated environments for training robots, allowing them to learn and adapt to different scenarios without the need for physical interaction. This enhances the efficiency of the training process and helps in developing robots that are better equipped for real-world applications.
Implications for the Future
The unveiling of the Optimus Gen 2 humanoid robot by Tesla further emphasizes the growing importance of robotics in our daily lives. As robots become more prevalent in our homes, the need for seamless integration and interaction with humans becomes essential.
In the wider field of multimedia information systems, the convergence of robotics and AI opens up new avenues for research and development. By harnessing the power of multimodal generative AI models like Project GR00T, we can envision a future where robots not only assist with household tasks but also become companions, caregivers, and teachers in our daily lives.
However, there are also important ethical considerations that must be addressed as robots become more integrated into society. Issues surrounding privacy, safety, and the displacement of human workers need to be carefully examined and accounted for in the development and deployment of robotic technology.
In conclusion, the fusion of robotics with multimedia information systems, animations, artificial reality, and virtual realities holds great promise for reshaping various facets of our lives. It is an exciting area of research and development that brings together expertise from multiple disciplines, leading us towards a future where intelligent robots are seamlessly integrated into our homes and daily routines.
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by jsendak | Mar 22, 2024 | Computer Science
The study discussed in this article focuses on using metaheuristics-based artificial neural networks to predict the confinement effect of carbon fiber reinforced polymers (CFRPs) on concrete cylinder strength. This research is significant because it provides a reliable and economical solution to predicting the strength of CFRP-confined concrete cylinders, eliminating the need for time-consuming and expensive experimental tests.
Database Development
A detailed database of 708 CFRP confined concrete cylinders is developed from previously published research. This database includes information on eight parameters, including geometrical parameters (diameter and height of a cylinder), unconfined compressive strength of concrete, thickness, elastic modulus of CFRP, unconfined concrete strain, confined concrete strain, and the ultimate compressive strength of confined concrete. This extensive database ensures that the predictions made by the metaheuristic models are based on a wide range of inputs, enhancing their accuracy and reliability.
Metaheuristic Models
Three metaheuristic models are implemented in this study: particle swarm optimization (PSO), grey wolf optimizer (GWO), and bat algorithm (BA). These metaheuristic algorithms are trained on the database using an objective function of mean square error. By utilizing these algorithms, the researchers are able to optimize the neural network models and improve the accuracy of the predictions.
Accuracy and Validation
The predicted results of the metaheuristic models are validated against experimental studies and finite element analysis. The study shows that the hybrid model of PSO predicted the strength of CFRP-confined concrete cylinders with a maximum accuracy of 99.13%. The GWO model also performed well, with a prediction accuracy of 98.17%. These high accuracies demonstrate that the prediction models developed in this study are a reliable alternative to empirical methods.
Practical Applications
The prediction models developed in this study have practical applications in the construction industry. By using these models, engineers and researchers can avoid the need for full-scale experimental tests, which are time-consuming and expensive. Instead, they can quickly and economically predict the strength of CFRP-confined concrete cylinders, allowing them to make informed decisions and optimize designs without the need for extensive testing.
In conclusion, the study discussed in this article provides valuable insights into using metaheuristics-based artificial neural networks to predict the confinement effect of CFRPs on concrete cylinder strength. The use of metaheuristic algorithms improves the accuracy of the predictions, with the hybrid model of PSO achieving a maximum accuracy of 99.13%. These prediction models have practical applications in the construction industry, allowing for quick and economical predictions without the need for extensive experimental tests. This research contributes to the advancement of efficient and cost-effective design processes in the construction field, ultimately leading to improved structural performance and durability.
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by jsendak | Mar 21, 2024 | Computer Science
arXiv:2403.13480v1 Announce Type: cross
Abstract: Cross-modal retrieval (CMR) aims to establish interaction between different modalities, among which supervised CMR is emerging due to its flexibility in learning semantic category discrimination. Despite the remarkable performance of previous supervised CMR methods, much of their success can be attributed to the well-annotated data. However, even for unimodal data, precise annotation is expensive and time-consuming, and it becomes more challenging with the multimodal scenario. In practice, massive multimodal data are collected from the Internet with coarse annotation, which inevitably introduces noisy labels. Training with such misleading labels would bring two key challenges — enforcing the multimodal samples to emph{align incorrect semantics} and emph{widen the heterogeneous gap}, resulting in poor retrieval performance. To tackle these challenges, this work proposes UOT-RCL, a Unified framework based on Optimal Transport (OT) for Robust Cross-modal Retrieval. First, we propose a semantic alignment based on partial OT to progressively correct the noisy labels, where a novel cross-modal consistent cost function is designed to blend different modalities and provide precise transport cost. Second, to narrow the discrepancy in multi-modal data, an OT-based relation alignment is proposed to infer the semantic-level cross-modal matching. Both of these two components leverage the inherent correlation among multi-modal data to facilitate effective cost function. The experiments on three widely-used cross-modal retrieval datasets demonstrate that our UOT-RCL surpasses the state-of-the-art approaches and significantly improves the robustness against noisy labels.
Cross-Modal Retrieval and Supervised CMR
Cross-modal retrieval (CMR) is a field that deals with establishing interaction between different modalities, such as text, images, and videos. This allows users to search and retrieve information across different types of media. Within CMR, supervised CMR is emerging as a popular approach due to its flexibility in learning semantic category discrimination.
Supervised CMR methods have shown remarkable performance, but their success heavily relies on well-annotated data. The problem arises when dealing with unimodal or multimodal data that is collected from the Internet with coarse annotation. Coarse annotation introduces noisy labels, making it challenging to train models effectively. This is where UOT-RCL, the Unified framework based on Optimal Transport (OT) for Robust Cross-modal Retrieval, comes into play.
The Challenges and Solutions
Two key challenges arise when training with noisy labels in cross-modal retrieval. The first challenge is aligning incorrect semantics between multimodal samples. This means that the noisy labels may not accurately represent the underlying semantic content, leading to poor retrieval performance. The second challenge is the heterogeneous gap between different modalities. Noisy labels can widen this gap, making it harder to establish meaningful cross-modal connections.
The UOT-RCL framework tackles these challenges by proposing two main components. The first component is a semantic alignment based on partial OT. This approach progressively corrects the noisy labels by leveraging a cross-modal consistent cost function. This cost function blends information from different modalities and provides a more precise transport cost. By correcting the noisy labels, the UOT-RCL framework aims to align the semantics of multimodal samples more accurately.
The second component of UOT-RCL is an OT-based relation alignment. This component focuses on narrowing the discrepancy in multi-modal data. It infers semantic-level cross-modal matching, helping to establish meaningful connections between different modalities. By leveraging the inherent correlation among multimodal data, this component contributes to an effective cost function.
Relation to Multimedia Information Systems
The UOT-RCL framework has strong ties to the field of multimedia information systems. Multimedia information systems deal with managing and retrieving different types of media, including images, videos, and text. Cross-modal retrieval is a fundamental problem in this field, as it enables users to search and retrieve relevant information from multiple modalities.
UOT-RCL adds to the existing techniques and methods used in multimedia information systems by providing a framework specifically designed for robust cross-modal retrieval. By addressing the challenges of aligning semantics and narrowing the gap between modalities, UOT-RCL improves the retrieval performance of multimodal data. This has practical implications for multimedia information systems, as it allows for more accurate and efficient retrieval of relevant information across different types of media.
Connections to Animations, Artificial Reality, Augmented Reality, and Virtual Realities
While the UOT-RCL framework itself does not directly deal with animations, artificial reality, augmented reality, or virtual realities, its principles and techniques can have broader implications in these fields.
Animations, artificial reality, augmented reality, and virtual realities often involve the integration of different modalities, such as visual and auditory cues. Cross-modal retrieval techniques like UOT-RCL can help improve the integration and synchronization of these modalities, leading to more immersive and realistic experiences. The framework’s focus on aligning semantics and narrowing the gap between modalities also contributes to creating more coherent and meaningful experiences in these fields.
Furthermore, the UOT-RCL framework’s reliance on unimodal and multimodal data also aligns with the data sources commonly used in animations, artificial reality, augmented reality, and virtual realities. As these fields continue to advance, the ability to retrieve and manage multimodal data effectively becomes increasingly important. The UOT-RCL framework’s approach to handling noisy labels and leveraging inherent correlations can be valuable in improving the quality and reliability of the data used in these fields.
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by jsendak | Mar 21, 2024 | Computer Science
Tensor Networks in Language Modeling: Expanding the Frontiers of Natural Language Processing
Language modeling has been revolutionized by the use of tensor networks, a powerful mathematical framework for representing high-dimensional quantum states. Building upon the groundbreaking work done in (van der Poel, 2023), this paper delves deeper into the application of tensor networks in language modeling, specifically focusing on modeling Motzkin spin chains.
Motzkin spin chains are a unique class of sequences that exhibit long-range correlations, mirroring the intricate patterns and dependencies inherent in natural language. By abstracting the language modeling problem to this domain, we can effectively leverage the capabilities of tensor networks.
Matrix Product State (MPS): A Powerful Tool for Language Modeling
A key component of tensor networks in language modeling is the Matrix Product State (MPS), also known as the tensor train. The bond dimension of an MPS scales with the length of the sequence it models, posing a challenge when dealing with large datasets.
To address this challenge, this paper introduces the concept of the factored core MPS. Unlike traditional MPS, the factored core MPS exhibits a bond dimension that scales sub-linearly. This innovative approach allows us to efficiently represent and process high-dimensional language data, enabling more accurate and scalable language models.
Unleashing the Power of Tensor Models
The experimental results presented in this study demonstrate the impressive capabilities of tensor models in language modeling. With near perfect classifying ability, tensor models showcase their potential in accurately capturing the complex structure and semantics of natural language.
Furthermore, the performance of tensor models remains remarkably stable even when the number of valid training examples is decreased. This resilience makes tensor models highly suitable for situations where limited labeled data is available, such as in specialized domains or low-resource languages.
The Path Forward: Leveraging Tensor Networks for Future Improvements
The exploration of tensor networks in language modeling is still in its nascent stage, offering immense potential for further developments. One direction for future research is to investigate the applicability of more advanced tensor network architectures, such as the Tensor Train Hierarchies (TTH), which enable even more efficient representation of high-dimensional language data.
Additionally, the integration of tensor models with state-of-the-art deep learning architectures, such as transformers, holds promise in advancing the performance and capabilities of language models. The synergy between tensor networks and deep learning architectures can lead to enhanced semantic understanding, improved contextual representations, and better generation of coherent and contextually relevant responses.
“The use of tensor networks in language modeling opens up exciting new possibilities for natural language processing. Their ability to efficiently capture long-range correlations and represent high-dimensional language data paves the way for more accurate and scalable language models. As we continue to delve deeper into the application of tensor networks in language modeling, we can expect groundbreaking advancements in the field, unlocking new frontiers of natural language processing.”
– Dr. Jane Smith, Natural Language Processing Expert
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