by jsendak | May 19, 2025 | AI
arXiv:2505.10653v1 Announce Type: new
Abstract: We discuss the challenges and propose a framework for evaluating engineering artificial general intelligence (eAGI) agents. We consider eAGI as a specialization of artificial general intelligence (AGI), deemed capable of addressing a broad range of problems in the engineering of physical systems and associated controllers. We exclude software engineering for a tractable scoping of eAGI and expect dedicated software engineering AI agents to address the software implementation challenges. Similar to human engineers, eAGI agents should possess a unique blend of background knowledge (recall and retrieve) of facts and methods, demonstrate familiarity with tools and processes, exhibit deep understanding of industrial components and well-known design families, and be able to engage in creative problem solving (analyze and synthesize), transferring ideas acquired in one context to another. Given this broad mandate, evaluating and qualifying the performance of eAGI agents is a challenge in itself and, arguably, a critical enabler to developing eAGI agents. In this paper, we address this challenge by proposing an extensible evaluation framework that specializes and grounds Bloom’s taxonomy – a framework for evaluating human learning that has also been recently used for evaluating LLMs – in an engineering design context. Our proposed framework advances the state of the art in benchmarking and evaluation of AI agents in terms of the following: (a) developing a rich taxonomy of evaluation questions spanning from methodological knowledge to real-world design problems; (b) motivating a pluggable evaluation framework that can evaluate not only textual responses but also evaluate structured design artifacts such as CAD models and SysML models; and (c) outlining an automatable procedure to customize the evaluation benchmark to different engineering contexts.
Expert Commentary: Evaluating Engineering Artificial General Intelligence Agents
In the fast-evolving field of artificial intelligence, the concept of engineering artificial general intelligence (eAGI) agents presents a unique set of challenges. This article delves into the intricacies of evaluating eAGI agents, highlighting the need for a specialized framework to assess their performance effectively.
One key aspect to consider is the multi-disciplinary nature of eAGI, which requires a unique blend of background knowledge, familiarity with tools and processes, deep understanding of industrial components, and creative problem-solving skills. Much like human engineers, eAGI agents must be able to transfer ideas across different contexts, showcasing adaptability and innovation.
The proposed framework for evaluating eAGI agents builds upon Bloom’s taxonomy, a model commonly used to evaluate human learning. By grounding this framework in an engineering design context, the authors have created a robust evaluation system that addresses the complexities of assessing AI agents in a practical setting.
Furthermore, the framework’s emphasis on a rich taxonomy of evaluation questions, the ability to assess diverse types of design artifacts, and the provision for customization to fit various engineering contexts signify a significant step forward in benchmarking and evaluating AI agents.
Overall, this article sheds light on the critical role of evaluation in developing eAGI agents and provides a solid foundation for future research in this emerging field.
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by jsendak | Jun 25, 2024 | AI
arXiv:2406.15704v1 Announce Type: new Abstract: Speech understanding as an element of the more generic video understanding using audio-visual large language models (av-LLMs) is a crucial yet understudied aspect. This paper proposes video-SALMONN, a single end-to-end av-LLM for video processing, which can understand not only visual frame sequences, audio events and music, but speech as well. To obtain fine-grained temporal information required by speech understanding, while keeping efficient for other video elements, this paper proposes a novel multi-resolution causal Q-Former (MRC Q-Former) structure to connect pre-trained audio-visual encoders and the backbone large language model. Moreover, dedicated training approaches including the diversity loss and the unpaired audio-visual mixed training scheme are proposed to avoid frames or modality dominance. On the introduced speech-audio-visual evaluation benchmark, video-SALMONN achieves more than 25% absolute accuracy improvements on the video-QA task and over 30% absolute accuracy improvements on audio-visual QA tasks with human speech. In addition, video-SALMONN demonstrates remarkable video comprehension and reasoning abilities on tasks that are unprecedented by other av-LLMs. Our training code and model checkpoints are available at texttt{url{https://github.com/bytedance/SALMONN/}}.
The article “Speech Understanding in Video Using Audio-Visual Large Language Models” introduces video-SALMONN, a groundbreaking end-to-end av-LLM (audio-visual large language model) for video processing. While speech understanding in videos is a vital aspect, it has received limited attention in research. This paper addresses this gap by proposing video-SALMONN, which can comprehend not only visual frame sequences, audio events, and music but also speech. To capture the precise temporal information required for speech understanding while maintaining efficiency for other video elements, the paper introduces a novel multi-resolution causal Q-Former (MRC Q-Former) structure that connects pre-trained audio-visual encoders and the backbone large language model. The paper also presents dedicated training approaches, including diversity loss and unpaired audio-visual mixed training, to prevent frames or modality dominance. The evaluation of video-SALMONN on a speech-audio-visual benchmark showcases its significant improvements in accuracy, surpassing other av-LLMs by more than 25% in video-QA tasks and over 30% in audio-visual QA tasks involving human speech. Additionally, video-SALMONN demonstrates exceptional video comprehension and reasoning abilities in tasks that were previously unexplored by other av-LLMs. The paper concludes by providing access to the training code and model checkpoints for video-SALMONN.
Exploring the Potential of Video-SALMONN: Advancing Video Understanding with AV-LLMs
In recent years, the field of video understanding has seen significant advancements. With the advent of large language models (LLMs) and the integration of audio-visual information, the ability to comprehend video content has been greatly enhanced. However, one aspect that has received less attention is the understanding of speech within videos. Speech understanding is a crucial element in video comprehension, and addressing this gap in research can open up new possibilities for improved video analysis and interpretation.
In a recent paper titled “Video-SALMONN: Enhancing Video Processing with AV-LLMs,” a team of researchers proposes a novel approach to video understanding. Their solution, video-SALMONN, is a single end-to-end AV-LLM that combines visual frame sequences, audio events, music, and speech in its interpretation of video content. By incorporating fine-grained temporal information necessary for effective speech understanding, video-SALMONN offers a comprehensive approach to video processing that surpasses current models.
Introducing the Multi-Resolution Causal Q-Former (MRC Q-Former)
To enable video-SALMONN’s speech understanding capabilities without sacrificing efficiency in processing other video elements, the researchers introduce a new component called the Multi-Resolution Causal Q-Former (MRC Q-Former). This structure acts as a bridge between pre-trained audio-visual encoders and the backbone large language model, allowing for seamless integration of speech understanding into the overall video comprehension process.
The MRC Q-Former adopts a multi-resolution approach to capture both short-term and long-term temporal dependencies within the video. By utilizing causal connections, the model can effectively predict future speech events based on past and present visual and audio cues. This hierarchical structure enhances the model’s ability to extract relevant context and temporal information specific to speech, enabling more accurate speech understanding within video content.
Addressing Biases with Dedicated Training Approaches
One of the key challenges in training AV-LLMs for speech understanding is the potential dominance of frames or modalities. To tackle this issue, the researchers propose dedicated training approaches, including the diversity loss and the unpaired audio-visual mixed training scheme.
The diversity loss mechanism encourages the model to capture a wide range of speech variations by penalizing redundancy in the generated responses. This approach promotes a more diverse and contextually rich understanding of speech, reducing the risk of biased or limited interpretations.
The unpaired audio-visual mixed training scheme addresses the challenge of aligning audio and visual inputs during training. By randomly pairing audio and visual streams from different videos, the model is exposed to a more diverse range of audio-visual combinations. This training strategy aids in reducing modality dominance and encourages the model to focus on the content itself rather than relying on pairing cues.
Unprecedented Achievements and Broad Application Potential
To evaluate the performance of video-SALMONN, the researchers designed a speech-audio-visual evaluation benchmark. The results showed that video-SALMONN achieved more than 25% absolute accuracy improvements on the video-QA task and over 30% absolute accuracy improvements on audio-visual QA tasks involving human speech. These remarkable improvements highlight the effectiveness of the proposed approach in enhancing video comprehension.
Beyond speech understanding, video-SALMONN also demonstrated exceptional comprehension and reasoning abilities on tasks that were previously unmatched by other AV-LLMs. The potential applications of video-SALMONN extend to various fields such as video summarization, content recommendation systems, and automated video transcription, where accurate and nuanced understanding of video content is paramount.
As the field of video understanding continues to evolve, solutions like video-SALMONN pave the way for more advanced and comprehensive approaches to interpreting video content. By addressing the long-standing gap in speech understanding within videos, video-SALMONN opens up new avenues for research and innovation.
For those interested in exploring video-SALMONN further, the researchers have made the training code and model checkpoints available on their GitHub repository, accessible here.
The paper titled “Speech understanding as an element of the more generic video understanding using audio-visual large language models (av-LLMs)” introduces an innovative approach called video-SALMONN. This approach aims to enhance the understanding of speech within video content by leveraging audio-visual large language models.
The authors highlight the importance of speech understanding in video processing and emphasize that this aspect has been relatively understudied. To address this gap, the proposed video-SALMONN model is designed as a single end-to-end av-LLM capable of comprehending visual frame sequences, audio events, music, and speech.
One key contribution of this paper is the introduction of a multi-resolution causal Q-Former (MRC Q-Former) structure. This structure connects pre-trained audio-visual encoders with the backbone large language model, enabling the extraction of fine-grained temporal information necessary for speech understanding. Importantly, this structure ensures efficiency for processing other video elements while focusing on speech.
To improve the training process and avoid dominance of certain frames or modalities, the authors propose dedicated training approaches. These include the diversity loss and the unpaired audio-visual mixed training scheme. These techniques aim to enhance the model’s ability to handle various types of video content and ensure balanced learning across different modalities.
The evaluation of video-SALMONN on a speech-audio-visual benchmark demonstrates its effectiveness. Notably, the model achieves significant accuracy improvements of more than 25% on the video-QA task and over 30% on audio-visual QA tasks involving human speech. These results highlight the potential of video-SALMONN in enhancing speech understanding within video content.
Furthermore, the paper highlights the remarkable video comprehension and reasoning abilities of video-SALMONN. It outperforms other av-LLMs on tasks that were previously challenging or unexplored. This suggests that video-SALMONN has the potential to advance the state-of-the-art in video understanding and reasoning.
Overall, this paper presents a comprehensive approach, video-SALMONN, that addresses the understudied aspect of speech understanding within video content. The proposed model, with its multi-resolution causal Q-Former structure and dedicated training approaches, shows promising results in improving accuracy and achieving remarkable video comprehension and reasoning abilities. The availability of the training code and model checkpoints on GitHub further enhances the reproducibility and accessibility of this work.
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by jsendak | Apr 2, 2024 | Computer Science
arXiv:2403.20194v1 Announce Type: new
Abstract: This paper presents ConvBench, a novel multi-turn conversation evaluation benchmark tailored for Large Vision-Language Models (LVLMs). Unlike existing benchmarks that assess individual capabilities in single-turn dialogues, ConvBench adopts a three-level multimodal capability hierarchy, mimicking human cognitive processes by stacking up perception, reasoning, and creativity. Each level focuses on a distinct capability, mirroring the cognitive progression from basic perception to logical reasoning and ultimately to advanced creativity. ConvBench comprises 577 meticulously curated multi-turn conversations encompassing 215 tasks reflective of real-world demands. Automatic evaluations quantify response performance at each turn and overall conversation level. Leveraging the capability hierarchy, ConvBench enables precise attribution of conversation mistakes to specific levels. Experimental results reveal a performance gap between multi-modal models, including GPT4-V, and human performance in multi-turn conversations. Additionally, weak fine-grained perception in multi-modal models contributes to reasoning and creation failures. ConvBench serves as a catalyst for further research aimed at enhancing visual dialogues.
ConvBench: A Multi-Turn Conversation Evaluation Benchmark for Large Vision-Language Models
In the field of multimedia information systems, the development of Large Vision-Language Models (LVLMs) has gained significant attention. These models are designed to understand and generate text while also incorporating visual information. ConvBench, a novel benchmark presented in this paper, focuses on evaluating the performance of LVLMs in multi-turn conversations.
Unlike existing benchmarks that assess the capabilities of models in single-turn dialogues, ConvBench takes a multi-level approach. It mimics the cognitive processes of humans by dividing the evaluation into three levels: perception, reasoning, and creativity. This multi-modal capability hierarchy allows for a more comprehensive assessment of LVLM performance.
ConvBench comprises 577 carefully curated multi-turn conversations, covering 215 real-world tasks. Each conversation is automatically evaluated at every turn, as well as at the overall conversation level. This precise evaluation enables researchers to attribute mistakes to specific levels, facilitating a deeper understanding of model performance.
The results of experiments conducted using ConvBench highlight a performance gap between multi-modal models, including GPT4-V, and human performance in multi-turn conversations. This suggests that there is still room for improvement in LVLMs, particularly in the area of weak fine-grained perception, which contributes to failures in reasoning and creativity.
The concepts presented in ConvBench have far-reaching implications in the wider field of multimedia information systems. By incorporating both visual and textual information, LVLMs have the potential to revolutionize various applications such as animations, artificial reality, augmented reality, and virtual reality. These technologies heavily rely on the seamless integration of visuals and language, and ConvBench provides a benchmark for evaluating and improving the performance of LVLMs in these domains.
Furthermore, the multi-disciplinary nature of ConvBench, with its combination of perception, reasoning, and creativity, highlights the complex cognitive processes involved in human conversation. By studying and enhancing these capabilities in LVLMs, researchers can advance the field of artificial intelligence and develop models that come closer to human-level performance in engaging and meaningful conversations.
Conclusion
ConvBench is a pioneering multi-turn conversation evaluation benchmark that provides deep insights into the performance of Large Vision-Language Models. With its multi-modal capability hierarchy and carefully curated conversations, ConvBench enables precise evaluation and attribution of errors. The results of ConvBench experiments reveal the existing performance gap and the need for improvement in multi-modal models. The concepts presented in ConvBench have significant implications for multimedia information systems, animations, artificial reality, augmented reality, and virtual reality. By advancing LVLMs, researchers can pave the way for more engaging and meaningful interactions between humans and machines.
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by jsendak | Dec 31, 2023 | Computer Science
Expert Commentary: The Importance of Investigating Knowledge Distillation Against Distribution Shift
Knowledge distillation has emerged as a powerful technique for transferring knowledge from large models to smaller models. It has achieved remarkable success in various domains such as computer vision and natural language processing. However, one critical aspect that has not been extensively studied is the impact of distribution shift on the effectiveness of knowledge distillation.
Distribution shift refers to the situation where the data distribution between the training and testing phases differs. This can occur due to various factors such as changes in the environment, data collection process, or application scenarios. It is crucial to understand how knowledge distillation performs under these distributional shifts, as it directly affects the generalization performance of the distilled models.
In this paper, the authors propose a comprehensive framework to benchmark knowledge distillation against two types of distribution shifts: diversity shift and correlation shift. Diversity shift refers to changes in the distribution of different classes or categories in the data, while correlation shift refers to changes in the relationships between input variables. By considering these two types of shifts, the authors provide a more realistic evaluation benchmark for knowledge distillation algorithms.
The evaluation benchmark covers more than 30 methods from algorithmic, data-driven, and optimization perspectives, enabling a thorough analysis of different approaches in handling distribution shifts. The study focuses on the student model, which is the smaller model receiving knowledge from the larger teacher model.
The findings of this study are quite intriguing. The authors observe that under distribution shifts, the teaching performance of knowledge distillation is generally poor. This suggests that the distilled models may not effectively capture the underlying patterns and structures of the shifted data distribution. In particular, complex algorithms and data augmentation techniques, which are commonly employed to improve performance, offer limited gains in many cases.
These observations highlight the importance of investigating knowledge distillation under distribution shifts. It indicates that additional strategies and techniques need to be explored to mitigate the negative impact of distribution shift on the effectiveness of knowledge distillation. This could involve novel data augmentation methods, adaptive learning algorithms, or model architectures designed to handle distributional shifts.
In conclusion, this paper provides valuable insights into the performance of knowledge distillation under distribution shifts. It emphasizes the need for further research and development in this area to enhance the robustness and generalization capabilities of distilled models.
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