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Titlebook: PRICAI 2024: Trends in Artificial Intelligence; 21st Pacific Rim Int Rafik Hadfi,Patricia Anthony,Quan Bai Conference proceedings 2025 The

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发表于 2025-3-21 18:05:21 | 显示全部楼层 |阅读模式
书目名称PRICAI 2024: Trends in Artificial Intelligence
副标题21st Pacific Rim Int
编辑Rafik Hadfi,Patricia Anthony,Quan Bai
视频video
丛书名称Lecture Notes in Computer Science
图书封面Titlebook: PRICAI 2024: Trends in Artificial Intelligence; 21st Pacific Rim Int Rafik Hadfi,Patricia Anthony,Quan Bai Conference proceedings 2025 The
描述.The five-volume proceedings set LNAI 15281-15285, constitutes the refereed proceedings of the 21st Pacific Rim International Conference on Artificial Intelligence, PRICAI 2024, held in Kyoto, Japan, in November 18–24, 2024...The 145 full papers and 35 short papers included in this book were carefully reviewed and selected from 543 submissions. ..The papers are organized in the following topical sections:..Part I: Machine Learning, Deep Learning..Part II: Deep Learning, Federated Learning, Generative AI, Natural Language Processing, Large Language Models, ..Part III: Large Language Models, Computer Vision..Part IV: Computer Vision, Autonomous Driving, Agents and Multiagent Systems, Knowledge Graphs, Speech Processing, Optimization..Part V: Optimization, General Applications, Medical Applications, Theoretical Foundations of AI.
出版日期Conference proceedings 2025
关键词Artificial Intelligence; Machine Learning; Deep Learning; Agents; Decision Theory; Large Language Models;
版次1
doihttps://doi.org/10.1007/978-981-96-0122-6
isbn_softcover978-981-96-0121-9
isbn_ebook978-981-96-0122-6Series ISSN 0302-9743 Series E-ISSN 1611-3349
issn_series 0302-9743
copyrightThe Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapor
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Conference proceedings 2025puter Vision..Part IV: Computer Vision, Autonomous Driving, Agents and Multiagent Systems, Knowledge Graphs, Speech Processing, Optimization..Part V: Optimization, General Applications, Medical Applications, Theoretical Foundations of AI.
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MLRQA: A Dataset with Multimodal Logical Reasoning Challengesto encourage the reasoning potential of multimodal large language models (MLLMs). Experimental demonstrate that state-of-the-art GPT-4v performs significantly worse than the human benchmark. Our dataset can also serve as an important benchmark for investigating multimodal logical reasoning in future. The dataset is available at ..
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Predicting Plain Text Imageability for Faithful Prompt-Conditional Image Generationalso developed a new visual-text dataset, named Ted1.6k, to facilitate model training and validation. Experiment results demonstrate the effectiveness of the proposed method in promoting prompt-guided image generation.
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0302-9743 Artificial Intelligence, PRICAI 2024, held in Kyoto, Japan, in November 18–24, 2024...The 145 full papers and 35 short papers included in this book were carefully reviewed and selected from 543 submissions. ..The papers are organized in the following topical sections:..Part I: Machine Learning, Deep
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MLRQA: A Dataset with Multimodal Logical Reasoning Challengesully understanding the content. Multimodal logical reasoning represents a significant research area within logical reasoning and recent methods focus on exploring the capacity of large language models to tackle this task. However, existing multimodal logic reasoning datasets lack rigorous manual val
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Fame Bias – Large Language Models Change Their Judgement Depending on Personal Name analysis tool. It extracts examples from NLI and story cloze task datasets with personal names, replaces them with names of celebrities, ordinary people or mixtures. Results show that altered names significantly influence model assessments, especially with more than two choices, indicating potentia
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