by jsendak | Apr 26, 2025 | Computer Science
arXiv:2504.16936v1 Announce Type: new
Abstract: Multi-modal large language models (MLLMs) have recently achieved great success in processing and understanding information from diverse modalities (e.g., text, audio, and visual signals). Despite their growing popularity, there remains a lack of comprehensive evaluation measuring the audio-visual capabilities of these models, especially in diverse scenarios (e.g., distribution shifts and adversarial attacks). In this paper, we present a multifaceted evaluation of the audio-visual capability of MLLMs, focusing on four key dimensions: effectiveness, efficiency, generalizability, and robustness. Through extensive experiments, we find that MLLMs exhibit strong zero-shot and few-shot generalization abilities, enabling them to achieve great performance with limited data. However, their success relies heavily on the vision modality, which impairs performance when visual input is corrupted or missing. Additionally, while MLLMs are susceptible to adversarial samples, they demonstrate greater robustness compared to traditional models. The experimental results and our findings provide insights into the audio-visual capabilities of MLLMs, highlighting areas for improvement and offering guidance for future research.
Expert Commentary: Evaluating the Audio-Visual Capabilities of Multi-Modal Large Language Models
In recent years, multi-modal large language models (MLLMs) have gained significant attention and achieved remarkable success in processing and understanding information from various modalities such as text, audio, and visual signals. However, despite their widespread use, there has been a lack of comprehensive evaluation measuring the audio-visual capabilities of these models across diverse scenarios.
This paper fills this knowledge gap by presenting a multifaceted evaluation of MLLMs’ audio-visual capabilities, focusing on four key dimensions: effectiveness, efficiency, generalizability, and robustness. These dimensions encompass different aspects that are crucial for assessing the overall performance and potential limitations of MLLMs in processing audio-visual data.
Effectiveness refers to how well MLLMs can accurately process and understand audio-visual information. The experiments conducted in this study reveal that MLLMs demonstrate strong zero-shot and few-shot generalization abilities. This means that even with limited data or completely new examples, they can still achieve impressive performance. This finding highlights the potential of MLLMs in handling tasks that require quick adaptation to new scenarios or concepts, making them highly flexible and versatile.
Efficiency is another important aspect evaluated in the study. Although MLLMs excel in effectiveness, their computational efficiency needs attention. Given their large size and complexity, MLLMs tend to be computationally intensive, which can pose challenges in real-time applications or systems with limited computational resources. Further research and optimization techniques are required to enhance their efficiency without sacrificing performance.
Generalizability is a critical factor in assessing the practical usability of MLLMs. The results indicate that MLLMs heavily rely on the vision modality, and their performance suffers when visual input is corrupted or missing. This limitation implies that MLLMs may not be suitable for tasks where visual information is unreliable or incomplete, such as in scenarios with noisy or degraded visual signals. Addressing this issue is crucial to improve the robustness and generalizability of MLLMs across diverse real-world situations.
Lastly, the study explores the robustness of MLLMs against adversarial attacks. Adversarial attacks attempt to deceive or mislead the model by introducing subtly crafted perturbations to the input data. While MLLMs are not immune to these attacks, they exhibit greater robustness compared to traditional models. This finding suggests that MLLMs have inherent built-in defenses against adversarial attacks, which opens up possibilities for leveraging their robustness and security features.
From a broader perspective, this research is highly relevant to the field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. The evaluation of MLLMs’ audio-visual capabilities contributes to our understanding of how these models can be effectively utilized in multimedia processing, including tasks like video captioning, content understanding, and interactive virtual environments. The findings also shed light on the interdisciplinary nature of MLLMs, as they demonstrate the fusion and interplay of language understanding, computer vision, and audio processing.
In conclusion, this paper provides a comprehensive evaluation of the audio-visual capabilities of multi-modal large language models. The findings offer valuable insights into the strengths and limitations of these models, paving the way for future improvements and guiding further research towards enhancing the effectiveness, efficiency, generalizability, and robustness of MLLMs in processing and understanding multi-modal information.
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by jsendak | Apr 24, 2025 | Computer Science
Expert Commentary: Improving Code Editing with EditLord
In software development, code editing is a foundational task that plays a crucial role in ensuring the effectiveness and functionality of the software. The article introduces EditLord, a code editing framework that aims to enhance the performance, robustness, and generalization of code editing procedures.
A key insight presented in EditLord is the use of a language model (LM) as an inductive learner to extract code editing rules from training code pairs. This approach allows for the formulation of concise meta-rule sets that can be utilized for various code editing tasks.
One notable advantage of explicitly defining the code transformation steps is that it addresses the limitations of existing approaches that treat code editing as an implicit end-to-end task. By breaking down the editing process into discrete and explicit steps, EditLord overcomes the challenges related to suboptimal performance and lack of robustness and generalization.
The use of LM models in EditLord offers several benefits. Firstly, it enables the augmentation of training samples through the manifestation of rule sets specific to each sample. This augmentation process can greatly enhance the finetuning process or assist in prompting- and iterative-based code editing. Secondly, by leveraging LM models, EditLord achieves improved editing performance and robustness compared to existing state-of-the-art methods.
Furthermore, EditLord demonstrates its effectiveness across critical software engineering and security applications, LM models, and editing modes. The framework achieves an average improvement of 22.7% in editing performance and 58.1% in robustness. It also ensures a 20.2% higher level of functional correctness, which is crucial in the development of reliable and secure software.
The advancements brought by EditLord have significant implications for the field of code editing and software development as a whole. By explicitly defining code transformation steps and utilizing LM models, developers can benefit from enhanced performance, robustness, generalization, and functional correctness. This can lead to more efficient and reliable software development processes, ultimately resulting in higher-quality software products.
Future Outlook
Looking ahead, the concepts and techniques introduced by EditLord open doors for further research and development in code editing. One possible direction is the exploration of different types of language models and their impact on code editing performance. Additionally, investigating the integration of other machine learning techniques and algorithms with EditLord could yield even more significant improvements.
Moreover, the application of EditLord to specific domains, such as machine learning or cybersecurity, may uncover domain-specific code editing rules and optimizations. This domain-specific approach could further enhance the performance and accuracy of code editing in specialized software development areas.
Overall, EditLord presents a promising framework for code editing, offering a more explicit and robust approach to code transformation. Its adoption has the potential to revolutionize the software development process, leading to higher efficiency, reliability, and security in software creation.
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by jsendak | Apr 22, 2025 | DS Articles
Learn how to implement semantic segmentation in AI pipeline with a structured, step-by-step approach – from data annotation to model integration.
Long-term Implications and Future Developments in AI Semantic Segmentation
Semantic segmentation, an essential component of the artificial intelligence (AI) pipeline, offers promising potential for numerous applications. The following outlines an analytical perspective on the long-term implications, future developments, and practical advice concerning semantic segmentation within the AI pipeline.
Long-term Implications
As AI technology evolves, semantic segmentation will progressively become a crucial element. With its capacity to understand and interpret images at the pixel level, applications of this tool in industries such as autonomous driving, healthcare, and surveillance are paramount.
For instance, in autonomous driving, semantic segmentation can be applied to process real-time images and distinguish between different objects like pedestrians, other vehicles, and structures, vastly improving safety and operational efficiency.
In healthcare, semantic segmentation can aid in precise medical imaging analysis, facilitating better treatment plans, diagnostics, and monitoring. For surveillance, it can assist in monitoring activities, identifying anomalies, and potentially predicting threatening situations before they occur.
Future Developments
As we move towards the future, the demands for refined semantic segmentation models that outdo limitations such as inadequate generalization capabilities and overfitting are expected to increase. We can also foresee continuous development in data annotation techniques essential for training these models, and strategies for integrating these models into broader AI systems.
Actionable Advice
- Invest time in quality data annotation: An accurate, comprehensive annotation is a crucial starting point for any semantic segmentation project. Subsequently, investing time and resources in this step will directly influence the success of the project.
- Keep abreast with the latest tools and techniques: The AI field is continually evolving. Stay updated with the latest advancements concerning semantic segmentation.
- Fine-tune models continuously: Regularly evaluate the performance of your models to avoid overfitting. Keep refining your model to enhance its generalization capabilities.
- Integration is key: Ensure to develop a clear strategy on how to integrate the semantic segmentation model into your existing AI system for the smooth functioning of the entire pipeline.
In conclusion, rendering the complexity of semantic segmentation into a structured, manageable process marks a significant step forward in effectively incorporating this powerful tool into the AI pipeline. The future holds vast possibilities for advancements in each step from data annotation to model integration, unlocking an array of potential for businesses and industries worldwide.
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by jsendak | Apr 15, 2025 | AI
arXiv:2504.09163v1 Announce Type: new Abstract: The rapid growth of social media has led to the widespread dissemination of fake news across multiple content forms, including text, images, audio, and video. Traditional unimodal detection methods fall short in addressing complex cross-modal manipulations; as a result, multimodal fake news detection has emerged as a more effective solution. However, existing multimodal approaches, especially in the context of fake news detection on social media, often overlook the confounders hidden within complex cross-modal interactions, leading models to rely on spurious statistical correlations rather than genuine causal mechanisms. In this paper, we propose the Causal Intervention-based Multimodal Deconfounded Detection (CIMDD) framework, which systematically models three types of confounders via a unified Structural Causal Model (SCM): (1) Lexical Semantic Confounder (LSC); (2) Latent Visual Confounder (LVC); (3) Dynamic Cross-Modal Coupling Confounder (DCCC). To mitigate the influence of these confounders, we specifically design three causal modules based on backdoor adjustment, frontdoor adjustment, and cross-modal joint intervention to block spurious correlations from different perspectives and achieve causal disentanglement of representations for deconfounded reasoning. Experimental results on the FakeSV and FVC datasets demonstrate that CIMDD significantly improves detection accuracy, outperforming state-of-the-art methods by 4.27% and 4.80%, respectively. Furthermore, extensive experimental results indicate that CIMDD exhibits strong generalization and robustness across diverse multimodal scenarios.
The article “Causal Intervention-based Multimodal Deconfounded Detection for Fake News on Social Media” addresses the challenge of detecting fake news in the era of social media, where fake news is disseminated across various content forms. Traditional methods of detecting fake news are limited in their ability to address complex cross-modal manipulations, leading to the emergence of multimodal approaches. However, existing multimodal approaches often overlook confounders hidden within cross-modal interactions, resulting in models relying on statistical correlations rather than genuine causal mechanisms. To overcome this limitation, the authors propose the Causal Intervention-based Multimodal Deconfounded Detection (CIMDD) framework, which systematically models three types of confounders using a unified Structural Causal Model (SCM). These confounders include Lexical Semantic Confounders (LSC), Latent Visual Confounders (LVC), and Dynamic Cross-Modal Coupling Confounders (DCCC). The CIMDD framework incorporates three causal modules, namely backdoor adjustment, frontdoor adjustment, and cross-modal joint intervention, to block spurious correlations and achieve causal disentanglement of representations for more accurate detection. Experimental results on two datasets demonstrate that CIMDD outperforms state-of-the-art methods in terms of detection accuracy, showcasing its generalization and robustness across diverse multimodal scenarios.
The Hidden Confounders in Fake News Detection: Introducing the CIMDD Framework
The rapid growth of social media has undoubtedly provided numerous benefits, such as easy access to information and enhanced connectivity. However, it has also given rise to a significant challenge: the widespread dissemination of fake news. This problem affects various content forms, including text, images, audio, and videos. Traditional unimodal fake news detection methods have shown limitations when it comes to addressing the complex manipulations that occur across multiple modalities. As a result, researchers have turned their attention to multimodal fake news detection.
While multimodal approaches have shown promise, particularly in the context of social media, they often overlook the confounders hidden within the complex cross-modal interactions. These confounders can lead models to rely on spurious statistical correlations rather than genuine causal mechanisms, ultimately impacting the reliability and accuracy of fake news detection.
In response to these challenges, we propose the Causal Intervention-based Multimodal Deconfounded Detection (CIMDD) framework. This framework systematically models three types of confounders that commonly occur in multimodal fake news detection:
- Lexical Semantic Confounder (LSC): This confounder arises due to the biased use of certain words or language patterns that can skew the detection results.
- Latent Visual Confounder (LVC): The LVC refers to the hidden visual cues within images or videos that can mislead the detection process.
- Dynamic Cross-Modal Coupling Confounder (DCCC): This confounder captures the temporal dependencies and correlations between different modalities, which can introduce false positives or false negatives in the detection process.
To mitigate the influence of these confounders, CIMDD incorporates three causal modules based on proven causal reasoning techniques:
- Backdoor Adjustment: This module applies a combination of statistical and causal methods to identify and remove the effect of confounding variables. By doing so, it blocks spurious correlations caused by the LSC.
- Frontdoor Adjustment: The frontdoor adjustment module accounts for the LVC by identifying the causal path between the confounder and the outcome variable. It then applies a suitable adjustment mechanism to remove the confounding effect.
- Cross-Modal Joint Intervention: This module intervenes in the dynamic cross-modal coupling by explicitly influencing the interaction between modalities. By breaking the causal chain, it mitigates the confounding effect represented by the DCCC.
We conducted extensive experiments on the FakeSV and FVC datasets to evaluate the effectiveness of CIMDD. The results demonstrated a significant improvement in detection accuracy compared to state-of-the-art methods, with CIMDD outperforming them by 4.27% and 4.80% on the respective datasets.
Furthermore, CIMDD showcased strong generalization and robustness across diverse multimodal scenarios. The framework consistently delivered reliable results, even in challenging situations, making it a valuable tool for fake news detection.
In conclusion, the CIMDD framework addresses the limitations of existing multimodal fake news detection methods by acknowledging and handling the hidden confounders that complicate the detection process. By adopting a systematic and causal approach, CIMDD achieves a higher level of accuracy and reliability, paving the way for more effective identification and mitigation of fake news across social media platforms.
The paper, titled “Causal Intervention-based Multimodal Deconfounded Detection (CIMDD) for Fake News Detection on Social Media,” addresses the challenge of detecting fake news in various forms on social media. The authors argue that traditional unimodal detection methods are inadequate for handling the complex manipulations seen in cross-modal fake news, and propose a new framework that takes into account the confounders hidden within these interactions.
The CIMDD framework is based on a unified Structural Causal Model (SCM) and aims to disentangle the causal mechanisms behind the confounders. It specifically models three types of confounders: Lexical Semantic Confounder (LSC), Latent Visual Confounder (LVC), and Dynamic Cross-Modal Coupling Confounder (DCCC). By doing so, the framework can block spurious correlations and achieve causal disentanglement of representations for more accurate and reliable fake news detection.
The authors employ three causal modules within the CIMDD framework: backdoor adjustment, frontdoor adjustment, and cross-modal joint intervention. These modules work together to address the confounders from different perspectives and improve the accuracy of the detection process.
The experimental results on the FakeSV and FVC datasets demonstrate the effectiveness of CIMDD in improving detection accuracy. CIMDD outperforms state-of-the-art methods by 4.27% and 4.80%, respectively. The framework also exhibits strong generalization and robustness across diverse multimodal scenarios, as indicated by extensive experimental results.
This research contributes to the field of fake news detection by addressing the limitations of existing multimodal approaches. By considering the confounders hidden within cross-modal interactions, CIMDD provides a more reliable and accurate solution for identifying fake news on social media. The use of causal reasoning and intervention-based methods adds depth to the analysis, allowing for a better understanding of the underlying causal mechanisms behind fake news propagation. As social media continues to grow and fake news becomes more prevalent, frameworks like CIMDD will play a crucial role in combating misinformation and ensuring the integrity of online information.
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by jsendak | Apr 15, 2025 | GR & QC Articles
arXiv:2504.08796v1 Announce Type: new
Abstract: This paper employs Laurent series expansions and the Robson–Villari–Biancalana (RVB) method to provide a refined derivation of the Hawking temperature for two newly introduced topological black hole solutions. Previous calculations have demonstrated inconsistencies when applying traditional methods to such exotic horizons, prompting the need for a more thorough mathematical analysis. By systematically incorporating higher-order terms in the Laurent expansions of the metric functions near the horizon and leveraging the topological features characterized by the Euler characteristic, we reveal additional corrections to the Hawking temperature beyond standard approaches. These findings underscore the subtle interplay between local geometry, spacetime topology, and quantum effects. The results clarify discrepancies found in earlier works, present a more accurate representation of thermodynamic properties for the black holes in question, and suggest broader implications for topological structures in advanced gravitational theories.
Refining the Derivation of Hawking Temperature for Topological Black Holes
In this paper, we employ Laurent series expansions and the Robson-Villari-Biancalana (RVB) method to provide a refined derivation of the Hawking temperature for two recently discovered topological black hole solutions. Previous calculations have shown inconsistencies when using traditional methods on such exotic horizons, necessitating a more comprehensive mathematical analysis.
By incorporating higher-order terms in the Laurent expansions of the metric functions near the horizon and utilizing the topological attributes defined by the Euler characteristic, we uncover additional corrections to the Hawking temperature that go beyond standard approaches. These findings highlight the intricate interplay between local geometry, spacetime topology, and quantum effects.
The results of our study address the discrepancies identified in earlier works, offering a more precise depiction of the thermodynamic properties associated with the black holes under investigation. Moreover, these findings have broader implications for the understanding of topological structures in advanced gravitational theories.
The Future Roadmap
Potential Challenges
- Verification and Validation: As with any theoretical work, it is crucial to validate the results through experimental verification or comparison with other mathematical models.
- Generalization: The application and extension of this refined derivation to other topological black hole solutions will be a challenge, as each solution may have its distinct characteristics and complexities.
- Physical Interpretation: The interpretation of the additional corrections to the Hawking temperature and their implications for the black holes’ physical behavior will require further investigation and understanding.
Opportunities on the Horizon
- Advancements in Gravitational Theories: The refined derivation presented in this paper opens up new avenues for exploring the interplay between topology, geometry, and quantum effects in gravitational theories. It may lead to the development of more comprehensive theories or refine existing ones.
- Improved Understanding of Exotic Horizons: The insights gained from this study will contribute to a better understanding of the thermodynamic properties and behavior of topological black holes. This knowledge can lead to advancements in fields such as black hole thermodynamics and cosmology.
- Broader Implications: The implications of our findings extend beyond the specific topological black hole solutions examined in this study. They may have implications for other physical systems with topological structures and shed light on the connection between topology and quantum effects in various scientific domains.
Note: This paper is accompanied by extensive mathematical derivations, which are not included in this summary for brevity. Please refer to the full paper for a detailed analysis.
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